{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:37:16.096616Z",
     "iopub.status.busy": "2022-02-20T13:37:16.095994Z",
     "iopub.status.idle": "2022-02-20T13:37:17.921852Z",
     "shell.execute_reply": "2022-02-20T13:37:17.921010Z",
     "shell.execute_reply.started": "2022-02-20T13:37:16.096574Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "from paddle.io import Dataset,DataLoader\n",
    "import paddle.nn.functional as F\n",
    "from paddle import nn\n",
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "import paddle.vision.transforms as T\n",
    "import time\n",
    "import json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **解压数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:37:20.911230Z",
     "iopub.status.busy": "2022-02-20T13:37:20.910225Z",
     "iopub.status.idle": "2022-02-20T13:37:26.386924Z",
     "shell.execute_reply": "2022-02-20T13:37:26.385968Z",
     "shell.execute_reply.started": "2022-02-20T13:37:20.911175Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!unzip -qo data/data89598/garbage.zip -d data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **将json文件读出为字典，方便识别垃圾分类**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:37:30.039445Z",
     "iopub.status.busy": "2022-02-20T13:37:30.038758Z",
     "iopub.status.idle": "2022-02-20T13:37:30.044920Z",
     "shell.execute_reply": "2022-02-20T13:37:30.044128Z",
     "shell.execute_reply.started": "2022-02-20T13:37:30.039393Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': '其他垃圾/一次性快餐盒', '1': '其他垃圾/污损塑料', '2': '其他垃圾/烟蒂', '3': '其他垃圾/牙签', '4': '其他垃圾/破碎花盆及碟碗', '5': '其他垃圾/竹筷', '6': '厨余垃圾/剩饭剩菜', '7': '厨余垃圾/大骨头', '8': '厨余垃圾/水果果皮', '9': '厨余垃圾/水果果肉', '10': '厨余垃圾/茶叶渣', '11': '厨余垃圾/菜叶菜根', '12': '厨余垃圾/蛋壳', '13': '厨余垃圾/鱼骨', '14': '可回收物/充电宝', '15': '可回收物/包', '16': '可回收物/化妆品瓶', '17': '可回收物/塑料玩具', '18': '可回收物/塑料碗盆', '19': '可回收物/塑料衣架', '20': '可回收物/快递纸袋', '21': '可回收物/插头电线', '22': '可回收物/旧衣服', '23': '可回收物/易拉罐', '24': '可回收物/枕头', '25': '可回收物/毛绒玩具', '26': '可回收物/洗发水瓶', '27': '可回收物/玻璃杯', '28': '可回收物/皮鞋', '29': '可回收物/砧板', '30': '可回收物/纸板箱', '31': '可回收物/调料瓶', '32': '可回收物/酒瓶', '33': '可回收物/金属食品罐', '34': '可回收物/锅', '35': '可回收物/食用油桶', '36': '可回收物/饮料瓶', '37': '有害垃圾/干电池', '38': '有害垃圾/软膏', '39': '有害垃圾/过期药物'}\n"
     ]
    }
   ],
   "source": [
    "f = open(\"data/garbage/garbage_classify_rule.json\",\"r\")\n",
    "file_dict = json.load(f)\n",
    "print(file_dict)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **创建数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:37:34.879198Z",
     "iopub.status.busy": "2022-02-20T13:37:34.878439Z",
     "iopub.status.idle": "2022-02-20T13:38:51.680268Z",
     "shell.execute_reply": "2022-02-20T13:38:51.679288Z",
     "shell.execute_reply.started": "2022-02-20T13:37:34.879162Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class GarbageDataset(Dataset):\n",
    "    def __init__(self,transforms=None,train=\"train\"):\n",
    "        super().__init__()\n",
    "        self.transforms=transforms\n",
    "        self.labels=list()\n",
    "        self.datas=list()\n",
    "        file_path=os.path.join(\"data/garbage\",train) \n",
    "        #遍历train下的文件夹\n",
    "        for file_name in os.listdir(file_path):  #file_name就是label,0、1、2、3、4、5\n",
    "            sub_file_path=os.path.join(file_path,file_name) \n",
    "            #遍历子文件夹下的每一张图片\n",
    "            for img_name in os.listdir(sub_file_path):\n",
    "                if \"DeepLearning - ┐ь╜▌╖╜╩╜.lnk\" in img_name or \"img_12074.jpg\" in img_name:\n",
    "                    continue\n",
    "                img_path=os.path.join(sub_file_path,img_name)  \n",
    "                #读取图片\n",
    "                im=np.array(Image.open(img_path))\n",
    "                if im is None:  #图不为空\n",
    "                    continue\n",
    "                if im.ndim==3:  #图片维度为3\n",
    "                    self.datas.append(im)\n",
    "                    self.labels.append(file_name)  #加入label\n",
    "\n",
    "    def __getitem__(self,index):\n",
    "        data=self.datas[index]\n",
    "        if self.transforms!=None:\n",
    "            data=self.transforms(data)\n",
    "        label=int(self.labels[index])#np.array([int(self.labels[index])])\n",
    "        x=paddle.to_tensor(label)\n",
    "        y_data = paddle.cast(x, 'int64')\n",
    "        y_data = paddle.reshape(y_data, (-1, 1))\n",
    "        return data,label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "transforms=T.Compose([T.Resize([224,224]),T.ToTensor()])\n",
    "train_dataset=GarbageDataset(transforms=transforms) # 训练集\n",
    "test_dataset=GarbageDataset(transforms=transforms,train=\"test\") # 测试集\n",
    "valid_dataset = GarbageDataset(transforms=transforms,train=\"valid\") # 验证集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **加载数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:40:58.282300Z",
     "iopub.status.busy": "2022-02-20T13:40:58.281371Z",
     "iopub.status.idle": "2022-02-20T13:40:58.286612Z",
     "shell.execute_reply": "2022-02-20T13:40:58.285677Z",
     "shell.execute_reply.started": "2022-02-20T13:40:58.282262Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "TrainDataLoader=DataLoader(dataset=train_dataset,shuffle=True,batch_size=100)\n",
    "TestDataLoader=DataLoader(dataset=test_dataset,shuffle=True,batch_size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **resnet网络结构**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:41:35.544106Z",
     "iopub.status.busy": "2022-02-20T13:41:35.543166Z",
     "iopub.status.idle": "2022-02-20T13:41:35.576835Z",
     "shell.execute_reply": "2022-02-20T13:41:35.576008Z",
     "shell.execute_reply.started": "2022-02-20T13:41:35.544067Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape          Output Shape         Param #    \n",
      "===========================================================================\n",
      "   Conv2D-6     [[1, 64, 128, 128]]   [1, 64, 128, 128]       36,928     \n",
      " BatchNorm2D-6  [[1, 64, 128, 128]]   [1, 64, 128, 128]         256      \n",
      "    ReLU-5      [[1, 64, 128, 128]]   [1, 64, 128, 128]          0       \n",
      "   Conv2D-7     [[1, 64, 128, 128]]   [1, 64, 128, 128]       36,928     \n",
      " BatchNorm2D-7  [[1, 64, 128, 128]]   [1, 64, 128, 128]         256      \n",
      "    ReLU-6      [[1, 64, 128, 128]]   [1, 64, 128, 128]          0       \n",
      "===========================================================================\n",
      "Total params: 74,368\n",
      "Trainable params: 73,856\n",
      "Non-trainable params: 512\n",
      "---------------------------------------------------------------------------\n",
      "Input size (MB): 4.00\n",
      "Forward/backward pass size (MB): 48.00\n",
      "Params size (MB): 0.28\n",
      "Estimated Total Size (MB): 52.28\n",
      "---------------------------------------------------------------------------\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape          Output Shape         Param #    \n",
      "===========================================================================\n",
      "   Conv2D-10    [[1, 64, 128, 128]]    [1, 64, 64, 64]         4,160     \n",
      "BatchNorm2D-10   [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "   Conv2D-8     [[1, 64, 128, 128]]    [1, 64, 64, 64]        36,928     \n",
      " BatchNorm2D-8   [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "    ReLU-7       [[1, 64, 64, 64]]     [1, 64, 64, 64]           0       \n",
      "   Conv2D-9      [[1, 64, 64, 64]]     [1, 64, 64, 64]        36,928     \n",
      " BatchNorm2D-9   [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "    ReLU-8       [[1, 64, 64, 64]]     [1, 64, 64, 64]           0       \n",
      "===========================================================================\n",
      "Total params: 78,784\n",
      "Trainable params: 78,016\n",
      "Non-trainable params: 768\n",
      "---------------------------------------------------------------------------\n",
      "Input size (MB): 4.00\n",
      "Forward/backward pass size (MB): 16.00\n",
      "Params size (MB): 0.30\n",
      "Estimated Total Size (MB): 20.30\n",
      "---------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 78784, 'trainable_params': 78016}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class IndentityBlock(paddle.nn.Layer):\n",
    "    def __init__(self,in_channels,filters):\n",
    "        super().__init__()\n",
    "        F1,F2=filters\n",
    "        self.stage=paddle.nn.Sequential(\n",
    "            paddle.nn.Conv2D(in_channels=in_channels,out_channels=F1,kernel_size=3,stride=1,padding=\"same\"),\n",
    "            paddle.nn.BatchNorm2D(F1),\n",
    "            paddle.nn.ReLU(),\n",
    "            paddle.nn.Conv2D(in_channels=F1,out_channels=F2,kernel_size=3,stride=1,padding=\"same\"),\n",
    "            paddle.nn.BatchNorm2D(F2)\n",
    "        )\n",
    "        self.relu=paddle.nn.ReLU()\n",
    "\n",
    "    def forward(self,x):\n",
    "        shortcut_x=x\n",
    "        x=self.stage(x)\n",
    "        x=x+shortcut_x\n",
    "        x=self.relu(x)\n",
    "        return x\n",
    "\n",
    "ib=IndentityBlock(64,(64,64))\n",
    "paddle.summary(ib,(1,64,128,128))\n",
    "\n",
    "\n",
    "class ConvBlock(paddle.nn.Layer):\n",
    "    def __init__(self,in_channels,filters,stride=1):\n",
    "        super().__init__()\n",
    "        F1,F2=filters\n",
    "        self.stage=paddle.nn.Sequential(\n",
    "            paddle.nn.Conv2D(in_channels=in_channels,out_channels=F1,kernel_size=3,stride=stride,padding=\"same\"),\n",
    "            paddle.nn.BatchNorm2D(F1),\n",
    "            paddle.nn.ReLU(),\n",
    "            paddle.nn.Conv2D(in_channels=F1,out_channels=F2,kernel_size=3,stride=1,padding=\"same\"),\n",
    "            paddle.nn.BatchNorm2D(F2)\n",
    "        )\n",
    "        self.shortcut_x=paddle.nn.Sequential(\n",
    "            paddle.nn.Conv2D(in_channels=in_channels,out_channels=F2,kernel_size=1,stride=stride,padding=\"same\"),\n",
    "            paddle.nn.BatchNorm2D(F2)\n",
    "        )\n",
    "        self.relu=paddle.nn.ReLU()\n",
    "\n",
    "    def forward(self,x):\n",
    "        shortcut_x=self.shortcut_x(x)\n",
    "        x=self.stage(x)\n",
    "        x=x+shortcut_x\n",
    "        x=self.relu(x)\n",
    "        return x\n",
    "\n",
    "cb=ConvBlock(64,(64,64),2)\n",
    "paddle.summary(cb,(1,64,128,128))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:41:42.033314Z",
     "iopub.status.busy": "2022-02-20T13:41:42.032651Z",
     "iopub.status.idle": "2022-02-20T13:41:42.154111Z",
     "shell.execute_reply": "2022-02-20T13:41:42.153437Z",
     "shell.execute_reply.started": "2022-02-20T13:41:42.033277Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------\n",
      "  Layer (type)        Input Shape          Output Shape         Param #    \n",
      "=============================================================================\n",
      "    Conv2D-11      [[1, 3, 224, 224]]   [1, 64, 112, 112]        9,472     \n",
      "     ReLU-9       [[1, 64, 112, 112]]   [1, 64, 112, 112]          0       \n",
      " BatchNorm2D-11   [[1, 64, 112, 112]]   [1, 64, 112, 112]         256      \n",
      "   MaxPool2D-1    [[1, 64, 112, 112]]    [1, 64, 55, 55]           0       \n",
      "    Conv2D-12      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-12    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-10       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-13      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-13    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-11       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "IndentityBlock-3   [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-14      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-14    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-12       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-15      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-15    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-13       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "IndentityBlock-4   [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-16      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-16    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-14       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-17      [[1, 64, 55, 55]]     [1, 64, 55, 55]        36,928     \n",
      " BatchNorm2D-17    [[1, 64, 55, 55]]     [1, 64, 55, 55]          256      \n",
      "     ReLU-15       [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "IndentityBlock-5   [[1, 64, 55, 55]]     [1, 64, 55, 55]           0       \n",
      "    Conv2D-20      [[1, 64, 55, 55]]     [1, 128, 28, 28]        8,320     \n",
      " BatchNorm2D-20    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "    Conv2D-18      [[1, 64, 55, 55]]     [1, 128, 28, 28]       73,856     \n",
      " BatchNorm2D-18    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-16       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-19      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-19    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-17       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "   ConvBlock-3     [[1, 64, 55, 55]]     [1, 128, 28, 28]          0       \n",
      "    Conv2D-21      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-21    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-18       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-22      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-22    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-19       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "IndentityBlock-6   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-23      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-23    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-20       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-24      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-24    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-21       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "IndentityBlock-7   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-25      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-25    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-22       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-26      [[1, 128, 28, 28]]    [1, 128, 28, 28]       147,584    \n",
      " BatchNorm2D-26    [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      \n",
      "     ReLU-23       [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "IndentityBlock-8   [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       \n",
      "    Conv2D-29      [[1, 128, 28, 28]]    [1, 256, 14, 14]       33,024     \n",
      " BatchNorm2D-29    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "    Conv2D-27      [[1, 128, 28, 28]]    [1, 256, 14, 14]       295,168    \n",
      " BatchNorm2D-27    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-24       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-28      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-28    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-25       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "   ConvBlock-4     [[1, 128, 28, 28]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-30      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-30    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-26       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-31      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-31    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-27       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "IndentityBlock-9   [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-32      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-32    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-28       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-33      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-33    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-29       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "IndentityBlock-10  [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-34      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-34    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-30       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-35      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-35    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-31       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "IndentityBlock-11  [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-36      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-36    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-32       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-37      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-37    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-33       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "IndentityBlock-12  [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-38      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-38    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-34       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-39      [[1, 256, 14, 14]]    [1, 256, 14, 14]       590,080    \n",
      " BatchNorm2D-39    [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     ReLU-35       [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "IndentityBlock-13  [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    Conv2D-42      [[1, 256, 14, 14]]     [1, 512, 7, 7]        131,584    \n",
      " BatchNorm2D-42     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "    Conv2D-40      [[1, 256, 14, 14]]     [1, 512, 7, 7]       1,180,160   \n",
      " BatchNorm2D-40     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-36        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    Conv2D-41       [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,808   \n",
      " BatchNorm2D-41     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-37        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "   ConvBlock-5     [[1, 256, 14, 14]]     [1, 512, 7, 7]           0       \n",
      "    Conv2D-43       [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,808   \n",
      " BatchNorm2D-43     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-38        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    Conv2D-44       [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,808   \n",
      " BatchNorm2D-44     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-39        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "IndentityBlock-14   [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    Conv2D-45       [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,808   \n",
      " BatchNorm2D-45     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-40        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    Conv2D-46       [[1, 512, 7, 7]]      [1, 512, 7, 7]       2,359,808   \n",
      " BatchNorm2D-46     [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     ReLU-41        [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "IndentityBlock-15   [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "   AvgPool2D-1      [[1, 512, 7, 7]]      [1, 512, 1, 1]           0       \n",
      "    Flatten-1       [[1, 512, 1, 1]]         [1, 512]              0       \n",
      "    Dropout-1          [[1, 512]]            [1, 512]              0       \n",
      "    Linear-1           [[1, 512]]            [1, 40]            20,520     \n",
      "=============================================================================\n",
      "Total params: 21,330,728\n",
      "Trainable params: 21,296,680\n",
      "Non-trainable params: 34,048\n",
      "-----------------------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 95.10\n",
      "Params size (MB): 81.37\n",
      "Estimated Total Size (MB): 177.04\n",
      "-----------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 21330728, 'trainable_params': 21296680}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class ResNet34(paddle.nn.Layer):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "    \n",
    "        self.conv1 = paddle.nn.Sequential(\n",
    "            paddle.nn.Conv2D(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=\"same\"),\n",
    "            paddle.nn.ReLU(),\n",
    "            paddle.nn.BatchNorm2D(64),\n",
    "            paddle.nn.MaxPool2D(kernel_size=3,stride=2)\n",
    "        )\n",
    "        self.conv2 = paddle.nn.Sequential(\n",
    "            IndentityBlock(64,(64,64)),\n",
    "            IndentityBlock(64,(64,64)),\n",
    "            IndentityBlock(64,(64,64))\n",
    "        )\n",
    "        self.conv3 = paddle.nn.Sequential(\n",
    "            ConvBlock(64,(128,128),stride=2),\n",
    "            IndentityBlock(128,(128,128)),\n",
    "            IndentityBlock(128,(128,128)),\n",
    "            IndentityBlock(128,(128,128))\n",
    "        )\n",
    "        self.conv4 = paddle.nn.Sequential(\n",
    "            ConvBlock(128,(256,256),stride=2),\n",
    "            IndentityBlock(256,(256,256)),\n",
    "            IndentityBlock(256,(256,256)),\n",
    "            IndentityBlock(256,(256,256)),\n",
    "            IndentityBlock(256,(256,256)),\n",
    "            IndentityBlock(256,(256,256))\n",
    "        )\n",
    "        self.conv5 = paddle.nn.Sequential(\n",
    "            ConvBlock(256,(512,512),stride=2),\n",
    "            IndentityBlock(512,(512,512)),\n",
    "            IndentityBlock(512,(512,512))\n",
    "        )\n",
    "        self.fc = paddle.nn.Sequential(\n",
    "            paddle.nn.AvgPool2D(512),\n",
    "            paddle.nn.Flatten(),\n",
    "            paddle.nn.Dropout(0.5),\n",
    "            paddle.nn.Linear(in_features=512*1*1,out_features=40)\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self,x):\n",
    "    \n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "    \n",
    "resnet=ResNet34()\n",
    "paddle.summary(resnet,(1,3,224,224))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **训练模型并保持**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T13:41:48.767951Z",
     "iopub.status.busy": "2022-02-20T13:41:48.766965Z",
     "iopub.status.idle": "2022-02-20T14:01:14.190489Z",
     "shell.execute_reply": "2022-02-20T14:01:14.189642Z",
     "shell.execute_reply.started": "2022-02-20T13:41:48.767893Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return (isinstance(seq, collections.Sequence) and\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:653: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 119/119 [==============================] - loss: 4.0335 - acc: 0.0810 - 245ms/step          \n",
      "Epoch 2/40\n",
      "step 119/119 [==============================] - loss: 3.3628 - acc: 0.1379 - 242ms/step          \n",
      "Epoch 3/40\n",
      "step 119/119 [==============================] - loss: 3.7486 - acc: 0.1685 - 242ms/step          \n",
      "Epoch 4/40\n",
      "step 119/119 [==============================] - loss: 2.8190 - acc: 0.1855 - 241ms/step          \n",
      "Epoch 5/40\n",
      "step 119/119 [==============================] - loss: 2.5412 - acc: 0.2271 - 242ms/step          \n",
      "Epoch 6/40\n",
      "step 119/119 [==============================] - loss: 2.8184 - acc: 0.2535 - 244ms/step          \n",
      "Epoch 7/40\n",
      "step 119/119 [==============================] - loss: 2.9684 - acc: 0.2737 - 243ms/step          \n",
      "Epoch 8/40\n",
      "step 119/119 [==============================] - loss: 2.7726 - acc: 0.3152 - 242ms/step          \n",
      "Epoch 9/40\n",
      "step 119/119 [==============================] - loss: 2.0474 - acc: 0.3260 - 243ms/step          \n",
      "Epoch 10/40\n",
      "step 119/119 [==============================] - loss: 2.4157 - acc: 0.3623 - 243ms/step          \n",
      "Epoch 11/40\n",
      "step 119/119 [==============================] - loss: 2.0524 - acc: 0.4011 - 243ms/step          \n",
      "Epoch 12/40\n",
      "step 119/119 [==============================] - loss: 1.8600 - acc: 0.4301 - 242ms/step          \n",
      "Epoch 13/40\n",
      "step 119/119 [==============================] - loss: 2.2534 - acc: 0.4673 - 243ms/step          \n",
      "Epoch 14/40\n",
      "step 119/119 [==============================] - loss: 2.0574 - acc: 0.4976 - 244ms/step          \n",
      "Epoch 15/40\n",
      "step 119/119 [==============================] - loss: 1.4340 - acc: 0.5367 - 243ms/step          \n",
      "Epoch 16/40\n",
      "step 119/119 [==============================] - loss: 1.3972 - acc: 0.5780 - 249ms/step          \n",
      "Epoch 17/40\n",
      "step 119/119 [==============================] - loss: 1.3856 - acc: 0.6330 - 243ms/step          \n",
      "Epoch 18/40\n",
      "step 119/119 [==============================] - loss: 1.1811 - acc: 0.6733 - 251ms/step          \n",
      "Epoch 19/40\n",
      "step 119/119 [==============================] - loss: 1.0048 - acc: 0.7198 - 245ms/step          \n",
      "Epoch 20/40\n",
      "step 119/119 [==============================] - loss: 0.9065 - acc: 0.7640 - 243ms/step          \n",
      "Epoch 21/40\n",
      "step 119/119 [==============================] - loss: 0.8727 - acc: 0.8019 - 243ms/step          \n",
      "Epoch 22/40\n",
      "step 119/119 [==============================] - loss: 0.3182 - acc: 0.8507 - 248ms/step          \n",
      "Epoch 23/40\n",
      "step 119/119 [==============================] - loss: 0.7256 - acc: 0.8542 - 242ms/step          \n",
      "Epoch 24/40\n",
      "step 119/119 [==============================] - loss: 0.3168 - acc: 0.8876 - 245ms/step          \n",
      "Epoch 25/40\n",
      "step 119/119 [==============================] - loss: 0.2573 - acc: 0.9098 - 241ms/step          \n",
      "Epoch 26/40\n",
      "step 119/119 [==============================] - loss: 0.6080 - acc: 0.9302 - 242ms/step          \n",
      "Epoch 27/40\n",
      "step 119/119 [==============================] - loss: 0.6064 - acc: 0.9315 - 245ms/step          \n",
      "Epoch 28/40\n",
      "step 119/119 [==============================] - loss: 0.4460 - acc: 0.9393 - 251ms/step          \n",
      "Epoch 29/40\n",
      "step 119/119 [==============================] - loss: 0.1897 - acc: 0.9569 - 242ms/step          \n",
      "Epoch 30/40\n",
      "step 119/119 [==============================] - loss: 0.2508 - acc: 0.9625 - 242ms/step          \n",
      "Epoch 31/40\n",
      "step 119/119 [==============================] - loss: 0.2041 - acc: 0.9693 - 243ms/step          \n",
      "Epoch 32/40\n",
      "step 119/119 [==============================] - loss: 0.3326 - acc: 0.9566 - 243ms/step          \n",
      "Epoch 33/40\n",
      "step 119/119 [==============================] - loss: 0.1621 - acc: 0.9561 - 244ms/step          \n",
      "Epoch 34/40\n",
      "step 119/119 [==============================] - loss: 0.0665 - acc: 0.9557 - 243ms/step          \n",
      "Epoch 35/40\n",
      "step 119/119 [==============================] - loss: 0.3635 - acc: 0.9672 - 244ms/step          \n",
      "Epoch 36/40\n",
      "step 119/119 [==============================] - loss: 0.1979 - acc: 0.9639 - 244ms/step          \n",
      "Epoch 37/40\n",
      "step 119/119 [==============================] - loss: 0.1068 - acc: 0.9652 - 243ms/step          \n",
      "Epoch 38/40\n",
      "step 119/119 [==============================] - loss: 0.0227 - acc: 0.9708 - 242ms/step          \n",
      "Epoch 39/40\n",
      "step 119/119 [==============================] - loss: 0.3156 - acc: 0.9791 - 245ms/step          \n",
      "Epoch 40/40\n",
      "step 119/119 [==============================] - loss: 0.0804 - acc: 0.9781 - 243ms/step          \n",
      "Eval begin...\n",
      "step 16/16 [==============================] - loss: 3.6536 - acc: 0.4021 - 137ms/step          \n",
      "Eval samples: 1522\n"
     ]
    }
   ],
   "source": [
    "model=paddle.Model(resnet)\n",
    "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()),loss=paddle.nn.CrossEntropyLoss(),metrics=paddle.metric.Accuracy())\n",
    "vsDL=paddle.callbacks.VisualDL(\"dir_log\"+time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) # 打印日志，方便过后查看损失率与准确率\n",
    "model.fit(train_data=TrainDataLoader,epochs=40,callbacks=vsDL,verbose=1) # 训练集训练\n",
    "model.evaluate(eval_data=TestDataLoader,verbose=1) # 测试集预测\n",
    "model.save(\"mymodel/resnet\") # 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T14:01:42.952785Z",
     "iopub.status.busy": "2022-02-20T14:01:42.951699Z",
     "iopub.status.idle": "2022-02-20T14:01:44.804249Z",
     "shell.execute_reply": "2022-02-20T14:01:44.803229Z",
     "shell.execute_reply.started": "2022-02-20T14:01:42.952743Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "model=paddle.Model(resnet)\n",
    "model.prepare(optimizer=paddle.optimizer.Adam(parameters=model.parameters()),\n",
    "                loss=paddle.nn.CrossEntropyLoss(),metrics=paddle.metric.Accuracy())\n",
    "model.load(\"mymodel/resnet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T14:01:50.159959Z",
     "iopub.status.busy": "2022-02-20T14:01:50.159175Z",
     "iopub.status.idle": "2022-02-20T14:02:10.348211Z",
     "shell.execute_reply": "2022-02-20T14:02:10.347356Z",
     "shell.execute_reply.started": "2022-02-20T14:01:50.159894Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict begin...\n",
      "step 1441/1441 [==============================] - 14ms/step          \n",
      "Predict samples: 1441\n"
     ]
    }
   ],
   "source": [
    "#对验证集进行预测\n",
    "result=model.predict(valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-20T14:02:53.576010Z",
     "iopub.status.busy": "2022-02-20T14:02:53.575382Z",
     "iopub.status.idle": "2022-02-20T14:02:59.318651Z",
     "shell.execute_reply": "2022-02-20T14:02:59.317831Z",
     "shell.execute_reply.started": "2022-02-20T14:02:53.575973Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵信息\n",
      "[[ 4  0  0 ...  1  1  1]\n",
      " [ 0 12  1 ...  0  1  0]\n",
      " [ 0  0 19 ...  0  0  0]\n",
      " ...\n",
      " [ 0  1  1 ... 18  2  0]\n",
      " [ 0  0  0 ...  1 18  1]\n",
      " [ 0  0  1 ...  0  6 17]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:101: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
      "  ret = np.asscalar(ex)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "计算各项指标\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.8000    0.1667    0.2759        24\n",
      "           1     0.3636    0.3243    0.3429        37\n",
      "           2     0.4222    0.6786    0.5205        28\n",
      "           3     0.4000    0.2222    0.2857         9\n",
      "           4     0.2745    0.3590    0.3111        39\n",
      "           5     0.7059    0.4138    0.5217        29\n",
      "           6     0.5745    0.6750    0.6207        40\n",
      "           7     0.5000    0.3056    0.3793        36\n",
      "           8     0.8571    0.1579    0.2667        38\n",
      "           9     0.5789    0.2821    0.3793        39\n",
      "          10     0.9412    0.8205    0.8767        39\n",
      "          11     0.5647    0.6486    0.6038        74\n",
      "          12     0.4242    0.4242    0.4242        33\n",
      "          13     0.0000    0.0000    0.0000         0\n",
      "          14     0.3636    0.1111    0.1702        36\n",
      "          15     0.3621    0.5000    0.4200        42\n",
      "          16     0.4000    0.2857    0.3333        35\n",
      "          17     0.2857    0.3226    0.3030        31\n",
      "          18     0.6429    0.5000    0.5625        36\n",
      "          19     0.8095    0.5484    0.6538        31\n",
      "          20     1.0000    0.6522    0.7895        23\n",
      "          21     0.1981    0.3182    0.2442        66\n",
      "          22     0.4894    0.6216    0.5476        37\n",
      "          23     0.2917    0.4516    0.3544        31\n",
      "          24     0.4242    0.4375    0.4308        32\n",
      "          25     0.4857    0.6182    0.5440        55\n",
      "          26     0.6452    0.5714    0.6061        35\n",
      "          27     0.2895    0.4074    0.3385        54\n",
      "          28     0.6500    0.3421    0.4483        38\n",
      "          29     0.5500    0.5238    0.5366        42\n",
      "          30     0.6410    0.7812    0.7042        32\n",
      "          31     0.3333    0.2444    0.2821        45\n",
      "          32     0.5789    0.3929    0.4681        28\n",
      "          33     0.2963    0.2500    0.2712        32\n",
      "          34     0.3556    0.4103    0.3810        39\n",
      "          35     0.8333    0.5714    0.6780        35\n",
      "          36     0.2162    0.3077    0.2540        26\n",
      "          37     0.4865    0.5625    0.5217        32\n",
      "          38     0.4865    0.4615    0.4737        39\n",
      "          39     0.6071    0.3864    0.4722        44\n",
      "\n",
      "    accuracy                         0.4455      1441\n",
      "   macro avg     0.5032    0.4265    0.4399      1441\n",
      "weighted avg     0.5051    0.4455    0.4523      1441\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用混淆矩阵查看预测结果与真实值\n",
    "#生成标签矩阵\n",
    "labels = []\n",
    "for i in range(40):\n",
    "    labels.append(i)\n",
    "def F1(x):\n",
    "    return np.argmax(x[0])\n",
    "yp=list(map(F1,result[0]))  #利用函数式编程，整理预测值\n",
    "y_pred=yp\n",
    "def F2(x):\n",
    "    return x[1]\n",
    "yt=list(map(F2,valid_dataset)) #利用函数式编程，整理真实值\n",
    "y_true=yt\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "#混淆矩阵的制作\n",
    "C = confusion_matrix(y_true=y_true,y_pred=y_pred,labels=labels)\n",
    "#混淆矩阵信息\n",
    "print(\"混淆矩阵信息\")\n",
    "print(C)\n",
    "#把混淆矩阵C加入index和colums，封装成dataframe数据格式\n",
    "df=pd.DataFrame(C,index=labels,columns=labels)\n",
    "#展示热力图\n",
    "sns.heatmap(C,annot=True)\n",
    "print(\"计算各项指标\")\n",
    "#计算各项指标\n",
    "print(classification_report(y_true=y_true,y_pred=y_pred,digits=4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **预测新图片**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-16T12:28:27.745198Z",
     "iopub.status.busy": "2021-12-16T12:28:27.744310Z",
     "iopub.status.idle": "2021-12-16T12:28:29.604359Z",
     "shell.execute_reply": "2021-12-16T12:28:29.603594Z",
     "shell.execute_reply.started": "2021-12-16T12:28:27.745156Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "可回收物/砧板\n"
     ]
    },
    {
     "data": {
      "image/png": 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aT34vb9LeClAAukg/s8ekFJQbC0pdP9eMuaAghOaLdkZ47ym4dV3ANIbAlZDcL+CK+hBACjxs93ZTjeZ1Zsit16YfOjtQJbeq5dwB0uvU0b1t2fgM7b9G4u68LejARkUSpDO5ggXrVEAMBDZAXleVzsuyYJ01xuAymUtx0pyBdV0BoHohKns/xOqCJvvej+nvDWjA52SbA0JKCSUleC6A90Pv8SAQKKiQ2XARonomE6OwRyZatOIG8K8jWPu+78/1OvL5loC49Xl/zv1nOkRNc673sw/pJwbR32OaArC1y52M2RNi3jk5ZxSLXXCbD9gSgm0RNFt437EePHJrQPq/NxNAdoFDN1qvIfTP91x8QK+R3DIxyCRSbzp+HSjs+3Y7kekKFDbHVNVZzQLYAnKpOpBpLhDkdcGSEtZlxrws1dd/OZ8wz1MlFdO6VrDuz+V/a44DbfrOj9ssgI4A9gC0lJJyCClBLDAJaJ6ivQcoBM9N4M1QMmmItHDcuF1vkctXc6kjCG00Xquyvw4UNmOMNrNLW/9VQ9Fe4RrW77/r54PQ9jm/rr01oACgqk/oVf7XsP3eYlQ/ecrzhgDqJ0XOub7vvRrUDeQtl07P3NdISWpBIvuMRwAoRcC0BZN+4e0nS78Q9qRUO3ezO7+u3eonPX8jDntQqP1FpGZBpyaz/V3PJ0ApyheklLDMC5ZFswjP5zNOpxMu0wV5XlBKhoc7D0OsOQm+4Huzi1mzIL3tvwMAElQg8P7fJ/z0z9j/tu8b5y6qnCXnkJTPQNxqlntwv/Xvfu4of7BNSOrd11sPwjWP1ASFgErjqAT9tc10MLNPNR0do/58xEEzat+wvTWgcGvRe0dHC2HuB0I7uD0oM0PS1qzoOYO9q3GvCeylyf4+9sTiXhP4Js+4f/+c2vhce8606Z91/xx1kXc8i0qisAEFRCX1QE5oMaKollKyufiy5gRM84zpcsE8X3B6+grn0wnrquo7w3NVzAQIGj+wNwvqfVfJfd3fegBqxqCDvIODP3cIwcTptUbWj23lRnah2H4Oibc5IO/7vs99se/v24/xe93/5nVjveeypEusAqgS5oBrEWR1HmAaZZeSLuLhwc9eb9/eElDoFluH3k1C5yq1pOz9uuY+o21245UU2i1wMY2kgg9Qr+shq/6bPvvslhSW7vU6Le0WKCiPUoBy2yS41baTyheBE6Aw6WzMuLSowir5HTiIlVmvyWMBCNt7k5KRllxNgHldsKQJ8zLhcr7gdD5jnS9Y5glFlNCLIVZQriYABxDHSliy8RMurcEtJ6BXw2t/VO/T835+8onQgwIrSQgCYnCtjjvg5Dpo9bn343OjTsFzgL7XBMtOE93f/622n1uAxv03rYaU5PTeYwJl47vq3GSARLOPQwD47zFNgSBgUdWnLcjW8SEExMEGqTBECCIFipOCnBM8+62PV/DfupoPXGsCG3vec9p3ngc/n34eLLCm0zw6WHCVz7/TZ9ktdLb49y7fo49f33Mh++b2K3PcPE//fQOCYFGGdtywJV/HMIKIzAUGSF6wJo0onJcF0+WM9XypXgO139eaned9dTzcVUAlUna+5w7QuWmJqMYe1D6U5l4OO1XXzQQ1XW6nCbcx3PZd0w6uNcB6fai3T118LRW5B6g+A7GS2zZeN4ODRCA5vbbwST/W/r5/fhVWmpbdS/5+fosLgSANAGVHcpt58abtrQCFvXx1QOjROOeCGBvTwh1JRETqtbihDfQgsZfSe1Dob6P/vOcW+jJwb6ruv671C8Kv8XXn7oOnVDu8oSYD5lYLTTsgQhyGyoM091iG5IyUMtbljGm+YJ5mjSc4PSFPM5Z1NU5FNTPmVs2pB5neZeeAwKSB61m24ev+DNuSZltA7T0N7vbcm37bF1eS9HWAcKWV0bUE3wNQDxT9GO0BygdGbgDCrfH3f28BvGuA/bzYaxLU/U7HfheCD6l9/ybtLQGF1ilXtmT3vRa4cAMD2KiBrDxtb+OllGqW3H7QeiLSJ2d2M6VDWf+3gcK2+AoAyDMTz9v+7/1kdtV4f+0rU6P7bfu8PU8/MZgtWCvEGsnJIWAchg44C1KascwLUloxTTOW+YzL5WSawYxlnsA2uYZgcf68TQq7VfgmDMPmnsRIWn/+vu0FgP/bk3PWc5vf9JK19if46vtb47lvGzCiLVDtf9MfuweM/l6bo+D6t8/Nh001KGlmYf9M/e89enZTB0SoxmeICJJoWbk3bT83KBDRjwH8pwA+gY7Wb4jIv0dE/yaAfwnAz+zQPyNaW+HrzthJ9bbo+odV7wFp4ZXYpEyM0YJ8tlKoV0/36mQ/OG5aSNou1uuBhhFwu/PzNSjcRv1nH71Kt1tgUj/f2b4tK7ERhb74A7N6EoYRey9OzpqdmFNCmiacTyczFS6YpzPm6awkHoDImo7OIWCIA+KghCH3C5GoaiZVQ+m+93wQkW1G3x4AvO21gfZFe/6e9wl2PWaGC8RbmqBzR9Jdx08r6O/lWnMBtvyA8lvlpnlgQ/r8cN+YE7fmTnvulmy198g4KPjxJWeUohnCxYjYFXKdkv2a9m00hQTgXxOR/52IXgL4q0T0l+y7Py8i//Ybn4lauTXAzQfArW5/aFWzpfqqc87VM8FMVw+u9n474a1F55Ppll14C9Vp/3e/KOx1K8BoAzDSJmF/TnKNp5v8NWZgF5Ciiy1sip2CGGQ+f48I5KG3R4FkKcLzPGvY8eMTnh4ftRbhuqLkBUUSNBmJNccAUROTotcxGEx6adQjyIDAnkFdm10fmC+du+cCrm3xPUhcL7amIda+IUueCmyk27aPegEAosrh9NcDvGKVAQJuawC95sKm/TT+odVd9Hnns8KUD797c1faM3fai3NBPn90EYgWhSWYq1jBUMd7S3J7eHcpgnVVt7GoffnG7mzgW4CCiPwegN+z949E9Degpd1/npNBq/2ypoky18XMrDZv5AEkBCkZRIKcCoZ4aBOss/cqMbUjZ4pdK5gq7rq3d6onoOSdqnWt3jZbkpmhWbUNwHSgt1pJrw77lPFWpaq5AitgONlqrx4XQmDEMFRNIYaAEiIoDgiDJ+wQGJoWrME9K5anR0yXC55OJ0znM9LljHnWcGAFn4Ihdq5EjmAad/a5LwDjdVgTgmCgAKLap97D/kx7LcE/68m75+xwEbtmaNzORmPb/8D6rdcMtP/LRvPqxzOXvDn2plQHNOnAXiSCEBvgu3lbsgDo+gqe3NdU/0w+npb3UV2HuuKFCxCK9mWgbk7rUUkEqYvbSKVgzQlJUj1NBBC+wRYvvxBOgYj+IIA/AuB/A/DHAPxpIvrnAPwVqDbxxZucR+PBBQFt4jlSV7IvBASihsiVqBMMhwFAMyGc/a4SH9tMyL2XQUpBwetzFMTs//67/cQRRZgrSdUkVu23jbpY4/qBmwtDRE2GVpJOA4JqvcoYjeBrYcElW6nylDDPM1599ilOJ+UM0pqA3FU+Ng5iGD21XIN5mIaN6lrv0f9/w2TaP/ObkrKvA4bXeRO+Cem71wA3gN3Nt/47oCuDtn8eoo3WU++ty0Fg976wZ1PaT7t72fMy+oYRhwjlFgANV3azpSBlYE1tX4psSWjuyfH7E74Gt+fatwYFInoB4L8G8K+KyCsi+vcB/Flo3/1ZAP8OgH/hxu/qvg+Ru5Jp3JAWuGZl22s7aL4vxDAMmnHXBcl017ziHPyVrCy4B8j4uQHsJEY3oM9MRs1u4ytJUyccmslw69luE3HmDg1cIwMDd24/Mv8/qV2Zc0GRjJK06vHZKgx99vu/h9WqEjNpXQoHhHEcwZFr2DGz5zlspwmZ6gpXm4k28fa3QOGbLNxebd/Y2uT5CLtIx9259yp/f0596Tj2Y9N7Gno3YQ8K9bzSSOn+uM31mUHRTQWqC7Mmt4nFEBiWVI2n03qMLgBzrCuiyFoDt3LOSBlIFrDV99sGYJiv8yFe074VKBDRAAWE/0xE/hvrkL/Tff8fAvjvb/1Wun0fjkOUPltNbOFdAwFVE4FINrEHumlIqPX6aOdZqMftpFm/cEUAwXXgTH9cCPFKT+0HQ7UWtIEnupqkfQae39ctnqL3kOhiiFZhKFpMxzY3oOQVZdW0W00QWpEWTVV+fHy0eocnMLGZH4wYxk2UIbh3MWpCkPRSB0AhC6llapP4mcV/S/2+MRc2f/fScttH3Oxt4Gb/3goW6sdIeSddcrfA/zmTob8v10x77akHD9fSsBvjKq2JwKIuy36sPVP06nrFTFuCkcQa/1ByQS7o/BzONW37z6NY37R9G+8DAfiPAPwNEfl3u89/aHwDAPxTAH7z60/WkpjcF773vfeAoD/ZdnjoJrO7IeOg5sQtqeML3fkDt/V7VdIJyN586e+lv7/t4zzvmrTH3d57n8VpxJCbCQ3IIqKVL3czxvtIRJBSRlkXpFV3SvJ9EebpZOHHK0oRHIZWICRwQIit0jERqQuT+/qT22cCUAvbCPU6nd++vPbvvh++7thbAuFNz7GX8BsAILk6tgeE/ZjuAUd27sYmqHRBhhhBgZF9gbrkll5bFPUccJsDIYQKHPWYovtetHsUlMzIRSDVK7HrX8OVOo5EG+D4uvZtNIU/BuCfBfB/EtH/YZ/9GQB/koj+MBSOfxvAv/ymJ6yLl7dqfp8tJ5VAVIIuxKig0h3DrLX59xJrr+71dmAvYfZhts9N1v79ZiLR9YTuibYq9epkiLWUF8RYZo9EZM8hGDGYVG9AMGFZtbzZsqwoy4LZzIRlWbCuC1Ka64SNgTGMQ52Aao6YR4E98amPk+84EJtc/ta/kjeYbrfAtB/vrWp/3deNTKRqWj7HVdw63y1Q6DVIBwSgaZO3tJ0974Du1YePB08iA1U3baUc/VxADT12Fy4zo3DjLIpofYqUNPNTYw/IyEwNYy8suBqA7hriX785Jnwr78P/+sylvtFeD935ADTVy9HXaySApNpxxAIvZEJsUXpDtPFpEreIbIM6sI0+2wc1EVRN612dt6RT3/bax03pZnakH88mjWtiUAjItsoqsFG3lwFbNSJmiBSUAuSccJ6eMM9T3TZtPU9Il6lGHxKpJyR0xUQcPCsweEYgd96DOh4KZgUFainYMbUEmC24LsDGn/F1QLoHif6zfV9v+vG1NSyur70vqrvXDG6N2yZVu/u3P74BO4FC2Kjr/e8DeANi/XNXk9HzTgKjkKAIVZcooA4Os1agBXKkCh09WahuTW9d8e06jtc63fPtrYpobA+KmupM1Fx/ziH4gtUinq2uvy9yd0fuy7ADnRtxJ62C+fbda9C33l3WoPeNn6yq4LdITg+8CdXcZFNBQ/VnEwTIGattZppTxjRdcLq8wqvHr3A5X5BLAdYESqVSHlryfGg8C7XQZJdonpPQez78XrV/CoowVM55lSZ/LgWGW+0WgO5t/D2ht49b2GpgTr1dn+dmr98wHZ5r/XX2JOb+Gn7srZT53j1qR1cPhG1BYufwebDNeBQRZOlMBSlAtmeAaRTdffi5riJsqlz8+n661d4qUBCT7LpQ++CWtuHKPlLxlhRItrNPf25fjK4q+kLvtQWv8d9LPWA7wb9uEl59bt/1UYW9lhBMU1AVzzQKIkB0cXsRUmWqVZVc5hlPpxNevfocp/O5ViUeOtdmjEpKxnFEHFryUTMdzN9NW7C6Dtqp1jP6GoH+bM+1PSg8tzhvcQJ7Sf26/r71+54jeBPSc7uQmyT39/tkqJ4HumU+1vcGuCqspUMF6Oe7eaX3rDtlV47DSAPVgjeRWf7mOeth0x/fpL1VoAB0WsEwdA/UOr63qX0y55w16aa0vSGzkYj94veMyb0/uKlzQCpS07P7Vs0PEZABVD95rs/V3Gi+8Jra7vEFFg+gvwbQ7Mg1rSg5a3mzdQVygeSCZZl1A5PTGcs6o+RsYc0BQ4yIvLVtQwwbINh7Ovb33kstNw24bsZD8JBbdbsSWpjSVjLvgXQ/OZ8zMfZz4hqcrr/ff7ZPsLoyW3i7mPt76TWY58ydHkT75p+XYmqBeW6KRUluKBoo0ENa0F3OGZl2/UUEjh63c0sI3gDn14Dpm7S3AhQUODvpYJ3U9mZowOCdEeOwMS2IqWoT6km4VlF2u4k+AAAgAElEQVTrgKHtH+nnY2P8i5QWDWZtM9jPTHAv7FknDYdqGngCUUsl1k1L+voPJXmdwYxcMlJasC66g9E8z8gpgYpUz8K6rvA6g3V3pBDqNmQOCn28xn4i9yG//bNqEJdJbUB3Qpbm3xfbNk6ssEdXkqCdW7oqQ13YcB3jru/etLmr2seg1FqMTVqWsh0fN8c2rauKVTWmDYg0ACglVyGxDZ6yvqxAswXcIoJcduHSsJlMTQwElA0oFNMi6jHceAdpFlT3KIxN+UURKyjbikB/kz4G3hJQAMg28PR/sZHyZN2ZUkKMOtFLSfqwIhAm5MwITC0ICYKctUOGGEExXGW+AdsIR5dIzKFDdQ8cUrKmGHrXvR0IOmhdiXPPYeCoWoHzGzX0uFb+ofqsOWvB02WesS4rzudHXC5nXC4XLMuCknS3JFclB/O86PbqWgrd4/8d4DxtWisY28RiahNFxIxdwOPoPUwZxbckI+NJm+SqngfjFKTAgs78c3TH+HvnIqxf6Tr+wd8+a2YUqUqJLzIUWGxJnUrwwCrvq6tzbY1LeyZq7407EQFybrENVNV4AigC3EXLUr9xkWoHxbQEiBUkFptDaNWZHQC0ilRB4aYJ+8NQ5XKoFRa2uy/dez0dVw0FJObyeJ77udXeDlAgIIxjNQGYUSsRAUDKCVwCvM8FBQSVioXUTZMTah26EAJKTjY+qnbp+yYdgKbSbwKcyNVjqHrMlp8PruXfa6p0sEzEwODYqg05wByOR3U3itupYRN8o3xBti3NLpjOFzx99UrjCqYzUl7h4apBCgKTlTw3F1YYMcSIIfhehtQBVLBdmI1AJSW9dNMifS4tS24ZjKSsNxmpVewzABASfZlmwW7wUCeVax1BauCBxpw7aVqHnKheR/9u8n6vItdF0pl2DYCkag46+AYE1d8P7BeEbgNHNpaii7F0nJYdp2SfPrd6X1r8jIQB0hHbkKLlU4tU0zXbnqgKUtrXtUPsrsrGRKGWO1K1AqpgUheLd6ydpCcaa26EQx8z8A0yJIG3BBSI2gYtIQQQBBQ8fJlNRe1r4FlWo6F2nzve1EFAhBCCuctEF1RPuPU2pf+uz5Xwe+u5jEI6Wj1HQN3GqT27PwwDAgcLnQZItLSc15L0SsTruuCrV1/i/HTCfL5gXRZAsiYlDV6a3JOg2i5XoBZz4KBAgTdgxzFsnrWUfMs8b0FhpgjcLBzyC259PIBrg7d4Gm+3uImeWKwPQNe/3bZmqoroQu5BAWam1D6AAkIjaC0cnzy5TedfErSislKstuLWXAOw9axu7pU2WZP6L9eFDnTj1BHkPbPBvN2+ThWGvyfNhy0RV9KKUlr1ZbfffLFXNj60wpjMvCuQ2ZJQ3JzoF7+35wimW8BApLkCThzWwCJz+W0WYwU4ApNOklruPGXklLCsC+ZpxmW+4KuvvsR8mcAgDKymQRy6GocEy3voTA/xACTTHoZ4HUTTpTC/SfOJ9E0Z62/fnid3bwHB5pc9mXjjzHvCsCe1nSupglf81XEgO48Dc4Cw/7ZAN6XJSKUr20aAiOaigFogk3QXo3aDFY33pKY7fcxQM07CfsysWlv30PU3V+P35sDw1oCCE4tAe8ZeYri7MEYN4BnGoarWtyZ9CJrlRwBKprqd+rUv+XbzhdUvdDL1XNA2MxmGQTdA3bH7DiylZKDbJGVZFizrinmaMNlrWWasadHKyYExxIBh1A1UKzkZfLuybjcjaUFQGugUa1pxfc5nwKBfSNv8D7laWG/q1rrF9NffETZjRER1y7TnztXfm5oPqDsn9efZgDxul92rx3t9zA0ZSR1JvI07EBHEQC0TtWoXWsQkpWSckJKcNciJvRqYlkfTcTAioaCac7XPBChoIFXHj8mCkaRqx3WOsWrAnrrvz99rQlQ5k68dvtreGlBwiV+8cIXZZTFGWywRAs0MGwYNIx2C5jY4uQi0xRpYQaERlajfb1jkG9qD/+1BP7p3YtiAQq/Z7F1Y/jzzPCMtC8qyYJomTUiatDy6exBc3QwBOI4DhmBehGjaghGIHJTDABMCmYYgsRaRZWZQbKpmY69vl0D7utaH53prcSNbFXc/jr1Z8CbX0N+VJq67c/XvdW1YMlbHX2w9SKhAuAf/agJCa3Q4+SnFNcuWfNbOS7bhrc4VB/qcF9urMqHkhFyChiDD3N91U1qfW6w5I1K0LoJJdA9HEhGU1LSWHtBu9mQdYyOCvb+kAaMSkzaG38CEeKtAIaVkbh5jbD1hKQC6F54YWRc2nQmg2v2V5afGIANbxrk3I5yRR2gpwJpFGKoGELrqRTAXEbOGDLvN7oE/ziJnjzGYZqyXsxKJ3R6KSsYBHMhSoAOGYTRuJYAiq386mBRz4gzdc8K4AgaCZyya58EeevPM+nslpetivB6I5sKz94RGDJKfxPu08l9uclWK60qCb++HbZHZt9Kf2BdHX6BWNqCUVdeuiWxe8g3dYtprLfUzIQChPhdZ33kMCQsjlwyBSubA6jqUIshZ96pMq4L7mjQrFVJAFADnALr5BBiJCwB1L08yvsArRrsL9/p+XwuvRPCV33dh+z2b0+bNhcJbAQo+4K6a6brTp3NJSpIxjgf9XKgi8K3iFuoFCBvB00+Sje0fAhBZS59zqJLWtYRQk65sc1qT1u5+6kmdnBQIUlowTxNOp5NqBpcLUkrdfQJhCHWzW+aIIYzgaHURgpoJujsT11cvyajuDyhQ1ktsEnMbfkKzX71vzbXioOTn9WeAtD0wANQENIggwBh5k0yuSXjknZO+IK3rOFiWar87V4tX2MVMlJYc5Wo44BNdGX0pu9BoiPW/jQO3+IE+/qCRqGYiSDBPUjeOTtDGAZwtdsS8DyWtALJthLMipVnfp4SUgSy2m7UnsdkGvIhBi6qI5YcIOUaYlsrw6AV95v08JoD4agz7dQOUutEtOSpsNC4CqIHOm7S3BhR84gCoAR5bGxU1nBdo0t5VcD9PS4LB5rg+uMQXV93DMOpW6MMwAFEqKPRRi5VfiMryuwVHooFHuk/CrJuj+JbrT09IOYMhWy0m6H6GzeMRtdycSfih4yjIah/4+2rzCtVtyENwVRoaX9CZQ7fce2RaTZFWY9A1NdgC9770jVs163JVIhiy3V+BGMVItp4LGMcRh8MBpRQcj0cQUd0J+nA44P7+vpoaN++TGvgTAGLp1GY0VhDmLgwO9ttIxY03g7kmgfagQLa1vNr9CraaHLcCOQOSNMq0qKZQ8mo7XusYoAoj2yw3DJAuBV2v3RuyRiDuisRuzVLfX/M1lax2PE3/b1+a8JuYj28FKABtQTOrpMubxKQmPXpX2bqum8H381TtgporMeykRwWHqFuqxzhgiAMobEnGnm9wQkggtbqRlAIxU+Hp6YRXr15hmS/IyTwhNkFjjDgcDqadKCj013HHUnNtDgYIDg6dOmncUXFV0+lp2k66fTq4L3IRwbquEAhWu/d1XZFLAQsQDGyfbOv4ZVlwthyL9957D3EYwNb/zIzj8Yh5WvDll18qcMSIVDKGYcD9/T0eHh7w4sWLjWZ3d3eHlBJevHjR+hY+2VVKavk53ximaOoFU1WTjdMHTEsBtyCt/QLZmBS7jE7Aaw9A05RzQsoJKc0KirICJSHlBTkniCSojCcEuHZCV4u5v5ecs2lcW1X+piZT5921eddrdSJys6Bvf+4rEHmD9taAQt+aILAJnYumlprN7qHMzWXZ8ht6lNzvV7gnpdR7MAIdu8wmJfdBTf77ZMVK1mXBsupOSnldcZkmPL56xDxPILPFx8OohODAGMdWIIU74HFJS7TfQyFsAEEb10UiUpCKFltVFbcYEZvqczaOQ3A6nWrFnnVdcTgcMIwjLvOEp6cnpJQQQsDRSsLP84zHx0csy1IZdgA15FqLuKxgItzd3WFdM7788kucTyeACFkEa1pxPB7xve99Dw8PDxVYQgh48eIFXr58iY8++ggvXrzA4XDYELclC5gHLX5KyrtIUCneaxMb0GZlmvbEcX8sgGrdVwEBaNBWAdKyIK0rUk7IeUEpCZAEFN0ZK5cEkYzRwtgDK4+A6gXqtJhu3tS5ZOUGdScqaaYctm5uncMCoQLBLbPB3lvUqT9P9Y7sXt+kvRWg0KN4RUvqyKkOJGqMeCkYhrjJeHQOYBgGjQ8wddLblnOIlnodgcr2B1DZ1uvrXwAguSCn1LkTF1xssqsGwhiHgMiMYRxVigwBx+PB3KQEItkWkrECqa4uV3LP+6aLBuwl/bQsmKyewjCOiBYivswzAGAcRz02qbfjeDhitu+YGdN0wVevXmFZFl2IUlBSwrrotvGu6q9WnyGEgJwzTk9PWGb9zTAMOJ/PINLAqnFUUElSkE5KHD88PAAAXr16hU8//RTzPOPu7g7vvfceXrx4gffefw8ffPAhPvzgA7x8+RJ3d3cYh0ObD2J7c4xAllzHYq/JMTMCGh/Vz68e4HNp0tPzWdZ5QS5FQa9onEwpCYIMkgyU1f4uMPqi8j5CDHEQD8GiQ7cSuxKaztT6fOnuU02HXfEXwLaSb5rUZqH3zC+2NSSctPz/HRSI6LcBPALIAJKI/INE9D0A/yWAPwitvvQn5GsqOlvIDJjaRqAlFwh57HiuHgXtn4auewJxGAYL7W2fObEVBq0xEMcBPA4I41B3PPaO1EzFbNGHDkK2rdq6IC1rLWyiCyc1UjIoGHjxWA1uCmAeUGsqerUckyBsFZO5Rm1q6e7LNAHw+0lI6wqP5pznCU+XM5gY02XChx98ADqMiDFgmRcAqMFSy7KglIIvP9eoye9//BGWYcXj4yNOT08ViNa0IqeMQIzxMCrbnrLtNq39n0rCcpkxXSYMQwQL4bIuGs5tQWUlq7tvCAPG8YDD8Q4EwjgcMYQBa1lx+uoJy2XG6atHfPXZF7h8ctYM0OmEcRzw8OIlfvDLfx/uHl6CKYIoYiknUJ4RUBDM9UexeTGILEvAGFA1FVrougcHEYrtnanP58CXswaVeQC2emnMi0VG9FNW7o8IQoxMDFDQMTRiWLwaOXN1dxIHeMg9nCA0HkgsAxJEEFLTI3liF5TvcDtCxDWdxrWp96b3+GzNBrFcizdtvyhN4R8VkU+7v38dwP8sIn+OiH7d/v7Xn/01qWuOmRAskYfAiCNBkCGSwZQr6aaLrEn9W7YTb6IBW6iyF2Yhm1ASmv2Zku6pmK2gyboswJqxzAvWtFqG4oSUVvQ8xzholuJg2YpeXNXNl74YDLOCgruiSikQS7pRlTWr67JoTYj7+3ur05cQmKCuWeB0ueDly5c4jkekZcEQI169+grruuJ8Ptfr5dzqUJ6fzrohi+izni8T1nltKqt9p/kVtsO3fcbELe/AuDjJgrwk5DUBA2qA1jAMSEknohQFt3E4IIYB94cHDBJxuVwAKihrRuEMFMHD/R3ef+8lhoFxnk54On+FD77/ET76+Ef44MNP8Ns/+ev46oufYoyEIFlL/Ueu29HVybRzifbmlMbBtKCjFnyUawq+OisDQE58FxSyEGRKCuogCOmOChR01yzYwiczJ6S6SVnPB3W3e9q5uyqdqIQDCVArccE0RV/TzbNkJgO2moBqJUaUOqdEb+55AL478+GPA/hH7P1/AuB/wetAQWCEiZsOnklI6qaxjukXWJ9j0A+6M/caZ2CBR13AUv97FJ3UztbnlLDOC2bLVjxfLkiz2phusuSygqiYq5IxhgHRip8Ow1jzHdyDUclNOAmasK4J8zJXk+NwuAOEsK5Jd/nJCfEwmrbU1OV17dT6kjGOI56eHpFTxhCC7uVg3gInNzeLAeraA3Q3Id9BqLfNPWKw1zA2UYXYRu4VU7mxLKoVp6QFYZmxLovyLecLZNQxOhyPNYFLoNcAgLSuOD894YeffIxPPvkY58sJy/KErz77fcThDj/+g7+GH//oD2B6+hIsBdGkbr/hL4B6T3sgcJI156xal1dE7kxFEQHHiEjbXa8FxcqkmQOYNeORY4RQUD6oJzhNQ3lOOLs22i9m/W3ndnTJj+erQPm5njMPemG5s6he234RoCAA/idSePoPREu3fyKtovPvQ/eb3DTq930IvOlBVwUBL72WMQzb8lc9oegLfpN/YNTt3jXXeyTUpVTqJE8GBvM01wjEbAvMF00Mzb1ZNYCofAGTpkv7QOVSsCSV3DFEjIeDLgIGXr7/PvLlAkBwmSdczjPGccT5fMLDw0OVutX8EcEyT3VyD8OAp6cnSFHyk9E8DH1cgHMQzhkEWzR5WTYEl/MFNYqUGx/jMRb+t/epu4g3LuFcsGLBMB4QmJFTxnK+mI9eCd1cRE2PoJvMpJRwenzCT3+fcBgHPDzc4eH+iENYcXr1Of7KT/42fuf3fopf+9Uf2Q5KXqFL8w72jLvfWy1c4nsk1NLoaeM+9eZzCNLiBHQsc9vxmgCCF+pRbUC3ygu1cvO+ZqLeX6+1FLgrktD6U71Q19sf9n1+q+3n+Ob9ztvxJu0XAQr/sIj8LhH9AMBfIqL/q/9SRISIru5K+n0fDlFcgwJ6tIcyz8HrA/AVILgW4BmKDgh9XMDeLeVSIRuptiaT0GnFdL5Un3yNY+9+zxZ96KZAiAcc7u8BwNTmhMfHR4gAv/Irv4Ivv/gC52kCM2PMmgPxg09+CcMw4vPPvwBzwOl0wjzPePnyJVLWclzLsmBd1lpK7XI+Y5knjONonQSrZs2YTmfcHY8AsPEU7CcJWy6IFJ3cKSVM81TNKrU/sdG2+tgQ9/r042S3UsFK4xPUZTkEnV7LvFihFv1djBE5pWoKkRGrl6cn/M5PfoLIwC//8BO89/77yu7nCf/vX/ur+PR3fgsff/QRvv/RB4hh1NDvjjTstYPqZu1AwYFASrvu3lNBZJwAqypPgKbxi8UU1NjE/tXCmc3E77m/nRlzLdn7MPlvuoA341Cv0217uPPkvUn71qAgIr9r//6UiP4CgD8K4O+Q7f9ARD8E8NPXn2UbgJFL1rBdCKSwBXls6wv6sQ4GfRzAMAyt9mHnpgGwmSDrNGsOwrLW8meufvu1xmG0jWaimQMBUvQ8Dw8PeHjxAAzA+XIBB0aaM87TBe+//wHmdcHpcgZHdfHN5sI8Txc8nk44XyZwCJjXFQJgmrUc+7KuAFAlhnsQXOKnlOARCX2IdSmiaju2lapda0kp4RAHrYodApLtH3mrKtJetXVtrJgLrP9M+QblBUrKSMuKwFphW0g1mYW1GpVGVPqWZ6qBxBjx8PCADz54HxwEr778EjEQji/eAwcgUsJ9XHB+/Aq/v64gFnz08fcwDBFimtA+UKcHhf47BaBtkJBrVZWXItQISQBWB6EYx2CgWOxAYsDqJCioelEW0vjo7VpBiyzUkxC2HhSvJtUqiu22NrwyH65dj9UxUcHCiMw3bN92h6gHACy6wewDgH8CwL8F4L8D8M8D+HP273/72vMAKhFEa3VE1rz/EDR8F4Bmi0kBwwNUeLPYQ4yI46jBLYOmDAsBqSh7LjYRZBYLRU4qfU1Fd4A5jncYhhF3d3cgAo7HOxyPx6pxPD4+qY/+dMacFsR1wOMXrypByMyQrD7o+TIhpax7AaohCRJ9LfOEsq5ASoAteMlWwNU2291k5eWiadsitarTi/sHqwdKqu2IQJgQWM2EJVn2owiQC7g4YWh7Z3R8AmCmWCVvC3JeoCy8aJk3gZV725oSAkGSbASaEpApCzjqM6S0YD6rljMcj+AhIpWC5bwoicmMcRjxySef4P0P38erx1eY5hlpAe4fBtwdjhiGgHVlzOuKL7/6CocXDzgKkJcLkPUekYv2AVBBwYER6OIAqJkKPag2lVvHCGLRnQRIjSxUbwQ8RFkIKIJEAFOxuWnDXXK3IMXQxov+eE0G1MzWIgLkvrbFdZVtb3sQ98/2AEHkVcLeYEFb+7aawicA/oLdWATwn4vI/0BEfxnAf0VE/yKAnwD4E68/jdS1L0UUfOEeCaCIpqZqjonHJHQFU7wAqlUWmtOKJa01nt7tatca/Nj7Fw94Gd/Hhx98oNmXlpV4OBwwTRMOh4Mu9nlGloJpmvCzn/0MAHA8HvHZF5+hUMF8nqo7jy0RJgSt73AYR2Rz1aEIBmKkddUJJ7oDNovuaSFGHkZmrOYKREANcqkxF0SgwIjM+OLzL3A6nXA4HJBKQQiEYEIqp4wwBIuxz4At+kCsbkNbkH3+CCxQZhiiPYP2o9hvnfQFOgJMvXKQ7GRqgaQMoYQDqb2dUkKmFXx/jzgMGCB4Oj2CpUAyakDVDz75JXzw0Uf49LPPEEIETJIehiPGMeCBIxAHrLlgJMIqGjxGmoRQgzufjxI0DxfpTk59lKu/qPkvN6ShugN9kQefuv0sNnND8xGkEuhOUZrHoco5rhG06Eje1/EHVyvna0hGBcHONn+D9q1AQUT+JoA/dOPzzwD8Y296HiJCxC6RJbK6gaDRjEsW8KAhuIe7e+MYgPv7B7z/wfsYY8SLFy9QAMzrgrvDsU6InDPO5zOYGU9PTzgcDhAR23l5xW/+5m/iBz/4AWIIiCHi8fER4zji448/rqG+MernOWe8fPkSMUYNsokDLnICBLhM6iYMQ6wET0oJBYI1JRyHEfNF8yLcn1yKgk0SwYsXL02yJlyezpjnGfzipeYNdPZ4ycryPz3pHpFuMsWcQSQI4wEQYE0rOBDieFAgssWRc0aScjUJvVjLEAfkrHtMFLPZPTDHQdU9Kxr8o2DNAciW3i45o8iMlFaM44giBefzjPHuDvEwYhhGvPfeS5y+eoVcCh5fvcLj4yNijPjw+9/DB9/7UDWsUvC3f+8nWOcLfvjjX8Xx4SVoGDDc3eHu4R48Es5fPaJAvUIkAkKLSO3nlIMYU3NbViDo+IViWYeFdM9SBoMLQ2pEZEHZm+0bXmIbbu5Eo9h1Y4hgUziESjXvihGPjbtRjuIWR9RzXRsP05UpIdWF/KbtrYhoFAEW0cKkgRhDYIzDgBgDjneaVPPypUa/ffT9j0BMGMegEzdnjIcR56cT0prxs88/RRgi0rLi1atXuL+/RykFl8ulSv4aCzAr4+/2d0oJQxzq+9PpVF1m3tx74ba9u5ayk1c2sVLOiCFgGAfM82Kei4Cz2fbj4QBPPAII49iSoHLKKCmDivZHDKrBFN8LoujOWYOBj9vO7uYDdFfkGCLu7u+1DFxHGFY3nDTXYr+AQgzmijX3pliGozP2O6KRA4NF1eEcNK1dRLmElh1p3oCUkdYVa05gViJzXVas64zPPvsMj4+PuH/xoJ6duxF344Dvf/9DfPr7Ez788H2M9y8xrStCJGTREmgIEWCBSNagJuoKkXTjVcfQpPZm4WwI5Y4XsGPVE+Hu7x0AsNsBtWOaYW9X9MaW3eoOiWyHOo9B0A+qlw0NDG5lBG/uo/69JcdJvslOkm8JKIyHEb/2D/z9CDHgxz/+McZ4gBTBOA44nb7SJJx4qH7zNWU8nV4hlRUPDw/49Pc+wzxN+PjDj3A+n3G4O+Iw6vHH4xEprZsFsRqRx8yYbdFnI92IUANwLpeL2tmd2u4D5JmDvb8eUCmUc2Po52lW4jQEjAd9rhACYghIIagy2rkGU85Ylhm+u/U4jnVBuwpfSql+cTGzIsaIZdZrkRQwDTgc1RRa57kSanoeXdiuXgZLCGMm2wlZK1cFZiRqYeHMmijlrl7XFHRhKM8TQwCEkBwss2obOWtJevWO6I7JJJpJeTlfUCTj088+xWeffYbvf/wx7u6OONwd8N6LF/gDP/4R1tMTAMHd3UFDqEvGMp0x5aJeoJEga8JAyqfQTm1206sCG3AlVb0VLihUDA40zDrUDY3J3N1Gdtu4gagW9PXP940AC283uCGpYCBALSiNHUjV+9U/2r2XbULUs6AA1CrQb9LeClC4f3jAH/ojfxivXr3Chx9+iM8+/RnSuuDxccUv//CH+OzTzyCkEyqaGpvWFTmtKGPWKL0CrEXZbO5y0EMIeHo6qa0dCE+PK+io4bdSiiY4pYx1WXA4HEEUQaRh0eualceQgpyKLbZuTwWLAuwHj5mNJyBIzlbTUZBKQvZinoKaNCUi4BCUzAwRy2VCmher9sRY1lndhSiaopwSggCcBcuyKhdhmk1aFqjBFYEQwVmwThPWZVFAATT4ijVWHxDEMQJBgFDAIWK+JI0h4AAOA8aBauHPjVZBHSAIgREQBjLNQsuVQQqYo4WIJ4RwQE4L1vlcOQscDnhCQJGMtCoglrJiiIxIA4gjPvj+R7i/f8Dl1Zd4+f5LIDJQCJKAILZDd2QkWiElK3EHAJJNgLdKUJoOnQCLBCwirXK4L6QCHT/TioqoT1LNBEYp0B23iTVqMUaw7Rzp9SwcUkRrPOv8sKhcNyNE9DqwaynvlCtoq5te6oJmERQSI9nNT0rB3KRSN50h1vNWk+EG8L2uvRWgABF8+umnWJYF4zji8fEVUpotlFY3RnHCybMZl0mZYYY+fOwyJ2PHjDMzDocR6zqDCBjHI0IY6q7Agc1Hb+48VTejJVXB2DOtDVlSAtkuzYACzjAO9TpeYzKlpBI9a/LMuq5aq4EISbLtCqUTMOeM8TgijoPl6WdABCmtKBDwyrh/uAdNpo0UIHIAC2FdlgpONRDGbNciugkt1XBX38gm66T2CtOhqb5r9rJ2TiQGZHPB9Xa314zsbVgvXd+0Bz+t7kwgJQMkGMeAQILjqCnGsmbc3R2Qz8mCjSwsW9QVta4at8HMWC5nSMkY4gElEYJjv9hCiRGyiCUQCUo24ppK5VJ09zAHBZPC4jPJwEFQx8cDDyrRWFpfgEjDmS0snyyhutaesh28HQT0TMWO07aptFwvsl3AJF2lbQcKKxIDi66UIjW1AhDlLIzc9viTN21vBSj4YAGomXkQYIgDzpcLiBi5CCK1yj4lF00+GgYsltffk18e6queiqSMtJYAACAASURBVKgLhANEEqbLBS+G+6ryD8NQbU9Vx7n+rZVM1CRw9S1bgJCTmH7vHvTjqvbj42Ml9+omr9CoPg+/TikBYn2AFn9RRJDWhHDfQm7JmPDAKh0cBN0ccmDsswghrb5ECCqRS8k1805jLlpUpxdTbQQdjIjX7z0mZOkAaTwcap/0JBlRY9pDDHh4uMOLFw9KfsaA8XhAWrL1N2ruRw82KVkdxJI1X0IEd8cjaAUWTWmt/c/WP9ZbINJn8zlWU8lxvcfk1ia/3ahb3O2zjmh0T4Xo3666ez3JW5GK3prn48Y1N/Uf3LTgzXfMDBI3Y6UC+c/T3gpQALYMOJt664uOma3qjXbqyK2YhkfwVeKvY9Q3KdBS4FmIcVB7OVui0d5nLSKYJvWr3x0GTNOlhTSHWI8BNE3ZAcQ5BvduzPMMDgFxHOo96URpXgBvTFoXcLCIxdXrVaKVpPNnqXUZuNtlu2OqfeFOyTQP61fJTWJo0Y+WfObSkrmpmtUONzDwoC4HP/+OiZDRdgMvReMqNhGoBIzjgA8//ABxCJjnC4Yh4sXDPc6PT6AzIVnVa0Bls4OK80CX8xl5TbiPg16DMzKw6QPlfTQOo8i2GpT433BNprkrqbPDe+6oH7f2Xs1LJRi9/oHnVkILrHTA0McM+N+9G9TvbbMWdiCl8SkKnhuS0TS7PhZDyn7fqG/W3gpQUK2pFUvRmIGmhvbElndkDBEimoxDFiM9G6HmHXR/f49hGPDq1SuEQFjmpU6gy+WCZJGEreZCi+N3ibssuujbXowdk2wTr2oVaEExVdp1dngt/BK4Vqu+u7tDuZwxLzMejvcIAFYAh8Nh88yuVfTA5cDimpHmWIQKGkTUSFKoIFuX2fgEd8MzQhgwjkeIiKro0hes2VaqUs6EKwDsn9lNPLG+bJNVPSwv33uBu7sDnp50k59DPOJyvuCLr77EsmruSb+I/JoigtPjE+bLhA+YtZgqa6CbCwd3j5ZSajThvkScJoZl6CZBba5sFz7r1m613661CK+TCXZQ8IxI0xy4cQX9OXr+qQcFv7ewu4/t/k/X99KbBrdI05+n8dcf8t233jfr0lFEzQMNw9XJpTUPmgvOTQTiFvlVw0O7iRLjgJwLPH8icMBq1YOItHKQ2sp9yXF09xFqzsG6trBaoA1SX2od6PZSMIm0zeoEnk6n6u93tXYcR8Su2Kk/n95HW5j9YvT366rxAB999BFGy2NQgHQXZLepblFCKnAL3/aCsH6t9iqVL7i/v8eLFy8q8Nbn7Sapm08htCK4DvTruiDGgLu7I168eIFhiDgeR3zw/ns43h11t6yUqiewmT2MGAdNUjudkZMmKLH1q1e1GqyP3TOkoHS9kNirp9b5t020639z6+Xj7mOrWgO1kGR/3dAS2vy+jj7sNSvCdg+K55rzIn2swrdtbwUogJQzJ4bun0gEUEAcjyghQIJuBFPyihCAlBZkFIQhWFabhQ9TQIwjirHOPqnY9mBMEEggxOMIilrCfRgG9TogguMIxIC8LghUwEHPWYpYrAFX8iYyW3itPoIwYSlJ6yBQU7mJthvE+KT96ssvm8QHAalgtPsBgDQvGq5rtQl7j4c/V29/D8OA7330EeJxxGy1K0spmJaMdTVpycCateJzsMKiEE1UyjlhWSaLt2iTTKC5/YWAeBiBwNB6RGIMe6sG1S8GorZPgpa5CygrkFZ1p/LAiAMj54SHhwd89L3vQ3LBfJmR5mzehaSl+HjAeHyA5AJZF7CRliWgJr/FIWK4O2K4v0PhgGnNyFljCq4DlLaJdYJ+gQIins+gLwJ3v3Vzw2pjkKVSd9W3hQkFgiQFBeY6NBDrgaAPra4cDuu+FMJaJbpA6mbGzaTq7tfcmr6BnWsq4uvKCMmrnbdf094OUIAgBMLxOOJyOQMADodjjfJygrZ3iaWclDFP6lJk1rp+UrTzU05VrXeNAKZOc9CCKHf3dyhFcLnMWqvR6vDndVWm3lU30VdgzcDMKWkQTkp6LDTO3X3NKeeaXxFCsDwKlWohRr2fbpEv5jJ88fCgps00qQeF1IUWh6HTVNZaZq0P32ZWTuIyzTo9qPq09LmCqbdCuimuaUzDMGKICkRiLlegkaOAuuS8MtW6rupaxTanoN/vsIadE23MuctlxuV8QS5ix2jw0uEw4gcff4yH+wd88fnneHp8gmRRgrOoZ+FwvMMQI7KliYdBPSCu0sVhwOF4wPHhAYe7OxAz7BS1Ne2GrqSy+wCr5Cevnn2956hzCG5OqtLQvA4iloBWco0I3UtzB/T+3lTrDeDIFQT8OvSM1uDRir6XRx9LWe8H38yseCtAQXzwhWr9Alf9vQyYqqVqYrSqQrkt9M5VtlgtAqCFiMKkbQxezFVwuVwQTcX2CS8imj1IJgFJk7TCEAEmDGOvmpNWNjYVOlqAkC9WDxvuo+lWS8v2QqW6wexavROesq3FWrhKQn+Wu7s7DMOg3gopyNAkqLVkLF7untoGI3X7MiLbVxKbbeX6zNM4xM3irkAWQo2sS9Xz0n5/OBzqmChIbStVsxFya1rweDpjTYJhvMMwaCg6R8LHn3yMH/3oV3A6PeGLL77APM8oFiXKzLi7u0MIAdOkNSWGOFRzQRl+rXM5jAMOd0dwDDXK1Nt+Yb/ORNgcx7T5/U2TwuZQ20oudWHkOsmd8L6l5vemyxbJ3tw7cr2uDIg6re5N2lsBCgRoxahSIKuW2apx6sy2SJt7i0gj73TLrlYYxL0Qy7JsbFInzvrF6VWKuSsuMgwDnk6nKi1SSobYQJICikElt6Vp+3mHYag1GUkRpoKC2P2GEHC5XIxdb6RqXwRFROp9kRF1vYfZ7fU6qdyuNUntsQNKuOkvue5DEFCkIFopOgdSwKVXU7GBxmG4utrH1scQMB7G6okYxxHH47ECnfe1AwoAECt4TcsCIcb9/Xu4u3thgJxwdzfih7/8S3jvvZf44ssvMM0Xi0/Qe3C+ZZqm5mGxknulFOSS6wLmGDAcDsimvtd51i327YK+XnQbUKPt3HFNyjUM3/OiL++WkmoJ/TW0wEtL4fZ+7qX4nmdwd/BNglFEM2+LgAX1tW9F8Np9O/ftrfA+QAQoBSmr9A5DtErIXtY9IYx3G9uwVA/E1v04hOHKdlObSifp5TTjeCQsywoxiSySkJOScatpGOOo5cW9wGeIQUuZFa/6s+1k9wCIqB5XctaNYEySfv7551Z8ReBFTr12g0++ZOSnlnoLRjQ2aQ7AJGjBvMygUYFosLwJ93YYEunEHQcUJkRWz0MMEWB214MSsEGjG3POLXpuR5Ttg7NKzroBLre4hD4WIIQDloXb3hysQTxryuAwqKYA5XpyXhED4wc/+BjzOkMkYZ4vFmClSnCMEYdxxLIuEHGtj5AD7P6VkM4Glse7I6bziDxNVatrGhDsX3frbadjb/IAsI1vmvqvP9D0crHMzAxB9ngTy1EJ0clrriC9ue6Nv59T86vr0oOgnMDcGAztcRzUNEz/zfb29PZ2gEJHCrpLxok6j7X3qkZeedcXe4y+n6JOhBACaHJJmOuEjUOsZcVSUjIt2jmZSSPTZGvrDcOAbPH5IUaky0XrFFjkpbtKo21+orZ+AaD7TD6dTnUXpNPpBKDFYaxmHokIjuZ+bG411RhijJV8XGiq0npdLU076j6XIEIyr0uRUi1k3eZOS7kFJqRVE7PcVvWCHzFELSZrvvYNCSctZqEGJpnUVPMoYIhx41ZWrYfQF43NJYNiwJoyLpcFzAOGAxn4LljTgvc++B6+P32I0+WMy3SBJ1GJhOoBmhY1K4gJkSNyAYZSrH4GKSiwpkW/fO89vDLOxyNYgWtQeC5daLtYtzstiSVMZXFQYBQ0MjA4b8Fah8K5hf15r69z2/5/FhQ0IOLZ8zFzi7B8w/ZWgAJRQAbU+4AMWQsOts2YSyeEEdP6CIIgimg4KTGKFdBkjkiS8fR0MvTUGPVSRNk/CVgXTaQaBnXDrfNsiwYIQRAhSPMMigFJgEM8gGNUFV3UlVdgATXLjCWvCKMCRzHWl5KAgzLIrrGslpAV2QpzsO5dKEELdyQAwaPRSOtIFFaPRpZSwUxKwTRPKCI4PNxhCLGSTIEZAXo+B8r/j7u3C7Vt29KDvtZ672PMudbaZ+99zrk/lfKnUmoULJICA/FFEQohiiT4EsyDJlEsA/qsiYKCT8EfRBCUEosYkIASoj5EMAgx+FAPZQwhiAlWUeW9t6ruuXffc/bPWnOOMXrvzYfWWu99zLXWPnufcy32tR8mZ6+55hpz/PTeemtf+9rXqlSkQMgbAcJIzFilopSIkKybcqTWg4JZxU9gJC+4WwpqPS4rBFyUnMMMCBVkWa36UYVWPLW5222rQNaM8+0tXr9+hWU7AykhcEJdVAFLCHj+/LkaJcNEWADJBRUBNB2w3N0in+/ARcMwEqhRYgNlYROJgenqBvHuBFoWrRcJhEICEU89D3H8Li3Zz93xJqU3k/EgBDmvps7ETaGJTcGZGSCrFmVz3X1Zvs0gqNHXc9Hjdcn/4S/0f804AaB9qAehLmsPQIVnfxc8BSL6h6G9HXz8LIB/F8AzAP8qgB/Y+/+2iPyVtx9N25dJVZ670453cS0xcqmYU+zuu4cRaQKHgPXuhG1bMc8TRGDcAHX7mQPEJNTmw4RlPTdALFuRkWoJxN0NZ6KdlXWZeaIOcC4WNmj9hGU7MEilidYGBCYUya2ev1ZpOXJNC5prSurK54FC7eDgtm3gqCKw7jkFwyPKpjUDDsKWUnEIAYSimRmpaqBYadYFXgNQoaIhjl/3zknBXVADFSvUUORgjXdRkIt6Lp4GC3bPbZ5Y1kO9wfPphFevXuL27g7Ts6eYpgn5pN6XV7Uqh8EyIrXqwiPgcH2F8uL7WJczpGRUL3SSChE3btoi0GtlPnr+HK8++0zb4YUAomq9GPYe0fiz4xjqNe7xFP9/KdbGzuZE9z4AQBeldxvzufp+MCEwJBff8pGHQciRoalH+F1oMCsifwfAz+t5UQDwPQB/GcCfAvCfiMh/9D7HC8xNRNQXXfsudKvpqbcRXCwlo9YurDFNM3IuLTb0FJ7+bmr1Ao5N1FoQqMedvhBzzkjzZPUPaPTkaACiA4kxBKxmSErOLV6vtWoPBc+c5NKASGsEqQvPjITXDpxOBbPps6aUkKaEal2b8pZxnLXCsxhQ6dcZODTPSsMvE67lDWXdAAs9Qkzq/aCDiKO8GjFBirnB1nE7xaTXTWgAGpM2LamltpCBXa1sjOFhGIUtqJMJzXz8yXPEaUKFYF20b6NrXmhKUysatXks4elHH+HNs2daLFZVlaqUimqNVWKIWr1JtTEunz1/jtOrl8CWuyfQWKl74dbOmO1pw1Hjcc8toJYqD7y/1rZQ0bGuPVC5W0fvs0zuDcJ944Z77z2EPDw+flzZh18A8Gsi8ptf/RDUwDEH93yBTtNkjVgUZHMrmG1hp6QrSEQwTZNlIXp60HsYAGidk5vqM2sqi203css+DUCnu//OkIuWPnRilIOMoe2ePeMQTdHY8921VtSsIqdw3cXaez76Z5i10Gia5waoerpSqqZNHfvw8MK5AY3paW58sUU//o6tFH2MYXUO7bMQHsKNVZGOoJdSdl2rATSWpLvjY4m5ewvL+YzlfNZQJWiI5JqKRNQyO9u2AigQ0UY4T58+xUcffYRlWbVxT62atpSekvR54CPEgOn6iC1njfehOzvzvqGQGwR/lg8pQXcDsd8kiNC4DzzMrXsz/IHF6+OhFCXsXN9nPJqleI/x4zIK/wKAvzj8/G8Q0d8iol8moufvcgCnE6vbqo1UFuPBk6V90FyiARSDIa1DZaOSldA8g3me24InAwq9YzKI7oFIXnfgYYwXOqkH0Y2Ag4EOHALq7o67z7IuVuiz7pqzaG69E6G00MmrNY0DYDl4L4RxY+Edjn2Mqb++wPW6ijWhHX/f+1b2hd/Cg9pbqfmxaq3Y1s0MUUfgfcJ1Re2ujDzurupxaPNVVb1aND2bt0G1SNPEYuFjzhnn8509mwJm4Hg82O82fX8wZiOnoHl0MUKI8NHz50iHDgzreNjtdiD7Ug16vE/dADgPo6dhH9u133V0XKNjGg+FOO/yamsL7+eRfG2jQEQTgD8C4L+zt/5zAP8ANLT4bQD/8SN/94tE9KtE9Kun02K7Oxl7rk9Sr87ThdhbplczItH7FdS6CwtEOtnGy5MBNKOxrqtKt9tCIMs7N+qxWf1kEx5QwMh3/zYBBuBn7LngRmTbNqzb2oBTz5w0+TRbWIfDQXPxIar6tLvoZhh8gqaB3dgeIu/7XvQQQhe4F3F5njzaAh0n0KjMBLL+EbXzPPwy3TMS8dSXFkB5qzydjL0lnhuEGGKD/c/nM754+RJffPESIsqArKJVpR46iGgxWrGmrs4n1yK1tfEqnCzFzZCGtgHEEBFTwiff/BQfPXvW5c70ge2u3+eKn0PnuOzmbPOcXLdjrK4dN4OLuf7OhuHLjMpXMQp4T2/hx+Ep/DMA/oaIfF+/X74vIkUUvfovoX0g7g0R+SUR+YMi8gePx1nR9y0jivbyI+4VgERARMUhRkQOIARTSdK4jq1ZrKcxvRDIC31SSirEAurkoFKROPQJHxlZSssY1Kpl1VKByNqkjEllxUZ3soUVdl21VqxlAxQA1+9YMyJxAwSHe2AufEWKAeuiNGARgrBpMxoYupjw6jzPqsa85Sa5HgzUosAozpmvSlmmYtmMUBWoNYJRLbWxLd34ALD3vHBKw6OYfAdU3EACA0EzPJEmTFFTwU6kYjbKL8Ea1kTt2ymaLajnBa8/e4HPP/sMqASiCZEYkhfk9YQYgZQYORec7xZIBVC1nqAyo4AhNSPWDK4FLBVBqv4bSuChqiSrQ5xxSE9wePIUGzM2CAQFypYrEGSUsmHbVjM2JkIjjFo1mwPA+n+KtoskVc1iUpl8VD9m0VrJUiG5Ns/uIYPwZW59M1Z66SoxP7w068HW4RoN5PWX/6yYGeDyce8yfhxG4Y9jCB1Im7/4+OcB/O13OUgtKrGuMamqHo03rZOKJgXozK0X0hy4u0i+y9+jkor2Lsi2E4VBTsxr+MlbgA15+JSmvusBTQfR3dzgKko0tKMzoFFEMQOy73fvB+jy6L4gRSpevnypbd3hu2sAQZvjZOtszWyt2JYFYkaQyYukdCGP2hMEYJ61oU2uVanMRK3Ixs+j58E7ot5cZOqVjqV4SfjIFDWQEoRkVZfRM0idsmPsQAKq4PXLl/j8hz+CQJCs1DtvG5Z1aV7guqy4vb3Fm9dvlMlYK9I8YzocLNXszV1gXHnxHF175kQKQKY4ocI6eEtFqdqz09vzqSeW7ZlUMPeXqxztXiN4ZynH3UJ/wMN423gfD4CIrCrT06kPj6+CJwA/nmYw/zSAf214+z8gop+H3pbfuPjdo8MRXaUQq3vsqPgopNKsZ1UmnoOONExcAEgpNne7lKItzGxXH+Nuf65OlfU42Dn97WH6oueu7BStuMnRdg6MWrTaUa9JC6WWZel8C6ClDN3t9O9w4G8ss2ZmbOuGUmp7n0jz5cQM5SIq+Ee2U2s7ebLYV7tc5U2LrsJQ4OMl0X79PokEPUYXz7N770MTd3UVa6cAl6rn4T+PBhBAw0ncEC/nBV+8eKGhm1d/1oLT3Z2GWFAJfjTPTcAsOByOYF/gFbtn78aK2lqtrVO2gp3+mX7+nnZ0mTU1hPuFJHLplptJoL72PTXqvx+H3+Mv8wwe/Zke/5yMn2nXbdqTQ83D+6RDv27fh1sAn1y89y9+xWNBC13CThuBLKYeG73Osyr6CtAyE76be8enlGYAvXvyfD0jBIYX2eiC1/bqDW3O2jXaAURF6q2iz+LHUiqyVWWKiNY8bLrbbtuGQ5yUG2FVmqVWbKcTrq0C0lmBY/mzYwRujBy3iEmByfP5DGBvLNrOLrLziKIVAhEHBJAJnkjDZcIgPnLRXLkdzxWdiRk1F2vjp4tcSLEWMcCRA6MumhYm68HIFiq1Pp7UadPat0Jbxp1PJwAApwCOAfm84nTSxr5e55BzxmyZoJgipuMVOEyoVbt/weTkPYPjojdAN3Rk1a+e/SgldwNhBge28BWwviQamSEYjAKDTS+xMwA8xAL23Jbx+YwbEg3zfPz9Y7jEg8O+nyyzoh5zbelSIYPk38Nh+CAKoiAae07T3HL/Xnqsu3JphS+ALR7mtlt7GXKtHZF2bMFDAwefLpFkAA3wy3lTtWjLWqyr/m2y73bQ8vXr1+3vvUbBJeL1oepkCdwFVxrd17AI30n3oFZXcQqhF0i9efNml24bP7u/Fmo0aSa9Bldd3vLW0pGl1AaqjqFWDxE6YconrQzIvUUe7e/1GmV3f4OLn6TU0rJ+be5qU1EdgjhPrVuTt/ODkcnu7u5axSRxwOF4jeP1NYgDSkXjrIjsybwtxLEJpi53fx5jtmkEtbUyNe1fQ/rysVDAPay3uewPAYGXn33s+I+CiP4zkXlP3Quyh/jgubxtfBBGQd3fgiq1AYE+CC6x3d3EaNRjwPQFLH6ttbYGJGlgPqrrLq3sVouVctvJHJDctty468ma0QDQTlLmHRBgKTKdSOu6Np3HBjq2uI+bsnS1nLrvluPk2XMFhk5F5vm4EenH6YDqmMEQaLYB5I13tSJyWRYs50XjaTu/cUGPx90bJl3AcFfUXH/VpOjNWdGYe7XxFqIBvM7bGCsGHYNoBsyqLgHVwVjMUzscj2YkTgAIaZpwuDri+vpGvRXpIrr9Hl4YBjegoOYl+bU2A8ZjFiGAOV28uoem1/DQJO44i2ctfL6O49HF/cDv3+nvqIOPFdLK6f3l2NH7jA+i9kEgWJERaNI6AGLAJn7buUJAPMwoECQTDImRUWsGUCFSQLkigRFTwmplxzEl1KJdiISAqzTjOM040e1ut9VdakPNQxeewJCgN5qZEcTKjzkobVaAbVkbDTevGw5JKdZKiBKECNRlRc4M1wTMJYMCmWqOUqPH0CGE0Bq5eru7wAELMzZvpsr7Sj4R7VO5lQLJGXQQEAlyWbHenSBrBgxVZxQNLSRqNyUCqBawCKZBU2E0UH6vatXaCA4BCNxkyNSNh6maa/akMUOr9lYE2bEZEBQsW0ZZMq7ihI0PENaKyZI3iADXV9f4gl6o5xADwmFGnAJ4qghZPZw1n7BuwFV4Yg1tAryVfA2MwoRNgAzNJASO2kNr8LI0a+IKyFpl2sRTiEAUdwZmN3fNQ6BKYFSow0YQqtp/Q78FAFDFal/GYYu2ey3UeQWkH2h6kMDuWbThZe/Wf+OSCFVB7+UxfBCegu+qDh7GGJr1nqZpZ9lLVWUiB+oOBxUcLZb+AXUXyvsuQNCUmKZp6gVGtqsooUa9CNdI9Jy+ipaI0ZpNvNW9klIQvW+EoAGRdlWY5sk8D/02n2Q87FgqgNLrJFw1enTlfXdy0A+gnZZjYw0COyMXgtYgZOM4OCir99frBgY2YuDuQg8AaAf2L1q4MzfykQN66mVo278wgKtKHLJ/2L3I2Tp1D56L9mksKLlgNjbnmzdv9NlMCWBSARPxrEB/VgSyBc1N28IzDg66jlqa/VouBVeGefmWXf1ydK+kh147w/1la2D47Jd992ikLr3Orzs+CKPgWYcRrU6eOhsu0t1mMoou0LGHvG2aajJgsZhc27quQOMssOWh8y4EcAAv2O42ItqzE4WkC350GjHhvJyxns+opWgarsmeoXWLHkuNVVOyS8s5VuFjxBpGubVSdHG7/sJY4zASpgjo12PpyhHYHIk2vQpP2vmNL89ydGTfm+24joNTwaXFsR56cOCmjCQQze0HAoyIJNDrPp3OyCWbZ+V6kaUZu3meFeSMSiQD0AFFaAbFS95HnIgIBi6KUbQ7CcsxglF1asRn3vZ6bFwuzvcxJuPnL3++Z1geMAYPGYevMz4Io0ADaOULZbMF4fUOANqDHw2GM9CWZdHCHNLJ7k1AYkqY0mQeR0at2hbN6bNNrg2amx+l3sQWtJJ4ury8g3OA1SNsBVKUZbkZ/ZYc2BQZAL7SEHu2VvW1VGQr1vHsRLsHBpI6GcuVpH3Rj+QYB/lCk6K/ABHtfB1H8QU/hgid0Ui7Y/p90lQlNe6C8zN8UV5qRvZn6uxPa2nGXitAeP36Fc6nMxzHgagsnlPcr6+v8cknn+D6+rp5Rw4iAj5vFPsIhnXY0+zX5qS1kndeyWXq18elofA9/m2xvo8RBB5fDy5YeviYbxu71PHFy+fk/t6/P9D4QWAKOyTc9AO2upnS8tzVe2znd0l2oCPaBKBw1wy05t2WAfBOQytEKu7u7lCNgNTSZlCjw4Sd284W2oyLzKm4y7Jgs47S1r3ccAxlIp5LgUhpHoJYB1EHB/07WshTe/1HsTSpT1yIWLuzqXE3PFZ1I+ILes9SlEac8rRmm0AEAxH7QtDbNpYIK2Ephn0c66GaGzY3lr7YpACF3SC5orYXHWlfz22reP36Nc7nM1KKrfp027aWjry+vsaz+RmePn2KwzzjvJ4M/KxgiiArlfbQC6W2wrFRcWn0BNik/MeMgp//YynBH+fYH//hnf0h7OKh99UwP378r+I5fBCewgi2eNw7uorOZnT3d7xBPgE8EOTA2Lbcq+bM3dcwQpDLZvp/pXUQcu6CM9p86KKwSsSSrUjn3NxOTxnS8HkavJQWmhDjfF4aG3BMb/pO5p93D2UkMum5VdTialMRTv/2R+5ofMsQjLsE9V3T2ZP+PWOKdkzTeXdoxXuoudztkXk2pFUPlpbKDRfn4oK2nhHyOJ6ZcXd3wt3prnXjBtQoLOczct5wdXWFZ88+xvWVaiwoIxKasgzaq4Is9FM8ZrrnLRARrq+vcXU8KiYj9xfOQzv8/1fjErt4aDzkBYxeQj/W20Oer+ItfCBG3RahfgAAIABJREFUgZCriorknIFSUNYFEaqGVPOmugTJxFGDc75Z6yDCDBRBFe9ZoAh5sVRmNoCSECGVEThCKrWiI3X1M0C1eQ6+gNZ1a+m0mAJi2ruWYRAOBbT565Y3FBFwmCASUSupoWKjl0i34B6TH+YZgoxaTXTWmtTGqHqOy+mkXaxDAJhaTwEiGI4hpk/IIA5WmV1RiVCFQaJiK8GML1g59e75uKFyA+spzrabwieqOhd6zwpKXVFlBZEBlUG7dteyQezFrCzPeb7CPB8RwgQYF1PbwS3IpSKGhEgTggDIZ8i6IKSI+foJYrxCQAKXCMpJu02jNlC9MqEGQphmhHhAigcknhDMGU4p4XA42v4TQIhgSm1O1EKQytA+GPcXpJbyyPC6nMIDHgAYMlsAKSBUECrAAhlfw1p9Gx7wUMjQ/65//0M4hKZ9lW71ruPDMApQJLpU60aEXo8wegLOxffdSHcb6pr40rsgEcFSTYScN7gAqrqJHUxjZlVjmqfmLjumoKFJhFR39VVQxSdK4w6IIKVJF47v4GKaB9IVn3s8P8at+m8lGuk5F+sXoF4GtXgd6GAgkQqgcAhNNty9CAAt28CsrMpczGDaQod5AADadXdNA30mveaDmqHw0chiVfklzngkZuWBbJ0s5Z29nWDlgC0zNfxAgeKeDcp5Qy4bvO+jU7nVY6pNoFZbt5HNoQwRZbk6IK1Gaq9VGHgAUi/GQx7E28aDi5HoAbOBfY8Gzzh+xfFVwNB3HR+EUSCoek3NGZHDzkK7OIpjB4fDoVU+jpRWFVd1t97apHFoXHwHJ/3li6xW7SsJ6hmQURiW7Ng+3DV1fMEnjU9CrUozIpEzA80o+HG8BJtIyam6QELLT2czTCMhyu9BGlKmHnZspsqk1Y+dyOMpV1+AXtjU7gP6JNqFYnA58mLEseFZkb3s52xCK37vCY8ToQAN0TyL5PjG6XRqVHU/J/VcVDcBJDtguJhqs9jkIXjp81CoZnUxIYZm0LQy9D7OsJuLF+99mWF4bFE+9Dd08fq64//XRkFEIEVBQQZMUae2CeULY1/b0JuywHa6lJJNEPUQ1DX2fox9srVy6iEf7YtpfKi6sPYLZrVsiE/6MU53fQYvqx35/3lg3o3X5aIsDrAC0F6J6FLj1YzEwZSYehpz6B9RuoqSeztjiDJ2O2pZHqtGHEHb3sOgNozHRz+ml+Rq5mRM6za8Bz2zMRpQvwfd21McZVnXoUO23s9ctpYxKmXbXZ8ep7aGOf5+raq+nVJq6L7Oj9xStg4sjkbdnwmG3/s8uIzRR4DS78vl+36M0Ti4bqM1Ymiko3fxRh76Xv27h0OZ8dm3if6O44PIPogI8roixITTtuB4nBFcNtyISJ5XdoDOVXqKNU4R20WmaUKs+nfCjDTNWPjc3PQYI66vr3E6nXA4HIw1mZtIx7L0zlI0LHhHq3WRhzb5PCbPueDpx8/x6vWr5m67RNt8mMGgNjGn5P0L9MG5/FjggG3VnL1LvlUja3ktgcarMHEY3c1hwGQuBSmG9jkXawnE2AYDFkLQ+1oKKtVdc1SRatTh3MRXfbhRLTVjW1Ysp1sEAqZpbsbjvCxg6wXhbfIA01VYtp0xjdZZfF03fPH55yB5oulKUpxlWU44nW61bf1yhxTnbhhyRt4ypsMBVzcTiij93MFgQHfjUgqoht41rFawybOPQOtDYzTe/rMactl1pYZ4m/hLWvNFBqFUuI/gy7nSsHiHwba53McQ9lmI6iAPupdD9+yE4H18kw/CKDQsIChwWJmRpQKW+uM0YYoJd29ucZwPiByAXMClgqNe7LptkBCQAc37hwQK2txkW1Y8eXKDit6ZRxecDCi7MxYjDlfXIFNXYvU+NZNRVTm6DlkEwGXfktXkV0XboWIfUitoisjnxYROVdYMVbEOJq0QlMiomZC3ini0dvWwxisimKOyJsu6GUWZwEWLioJ0MZBca5OFLyW3xqYKvBY4Xib2WU66ezkjNNJQYkwAiFGEEIkA67cAEcUAKozIpLTlkgtEzIsw4DHXAmFCjMpGFO50aSJpHZ9O5xMO5wnJGs8SAtalYDmvqOsG2jJEQhOr8R6jYh2hiILK5kvG+by2DYCooNYV23bGsp4RqwDaUADqPSrgiaG247GNdfRUSFxkRSsmM4nJx7LRyQWFK4gyuAYESWioDsHOXf/PoiEQCdTYBP1dZQZXARcjwoFQ2TqK6QmBqgKYDAaKEboqAVJNkAYg6tWx7zI+DKMAdOHToN2RtpKb+AaoU2yb61dqM8SjNQ0xItku5SGDu+5v7t60CTkiuRp6hMaic1CwuaSiwiUTxyZaCvRmr676PLrGLowKJkQEDQnsmJ7mAzT1KiKtFDlG7YItuZOq/JxOJ83dHw4HAz73pCqnaUsVZDEZuiE08OpE98BcT0Gl6RkiXbzUvaKd/gRbGXvpxlDvsxK3vGx4dJ9Ht7uL3ZquQh36d27KSmXWxjQxqCDOsiw4nc+YlzMO1JvWBtZSan/+FEb033ZjF4Kxc2FiAP2+dvfbGCYXO+7+c32MeEy7PpAWIEkxJSY21F9AVIGq7WIqNLQFkyYnSPMCJBkkBGIVonWDRyKgYa6HskGkgGoBJGt2gwDTK4NmVgiBAqQGzb7Q+0UQ72QUiOiXAfxzAD4TkZ+z9z6G9n34GaiYyh8Tkc9J7+h/CuCfBXAH4E+KyN942/EdlMs54zAfMB8OEADH4xHrujak2x9Wzlld3ko7N37bFt1Fhji5lIIQY8MiRnfRrSeRTXiR+0bDkO5t2zAfJ9VMOBwGUM4zHoTj8YjNQoSRPAN4nT7a+des15OmCWveumipqyvB6jMaKIkWX8eoYGrOtQGPHis7sr+uK+bD3PgEDZTcNms4il32QYZF4ffO77uHHMzcPBD/XAjaX9M3Xz+PXYqOuvalL2omQra/96zHum1Ic2jPo9aqFZ6mqRmC9qwAeRVr2mkZ7Bz3HdCqHjYzdZugmykeMgqXrvaXGYXGTKxKBZeqN4OFwLSBRUBimxRE0+GGNuoSroBlrSylogVkrEaF6wbxEy8FUjNINpBsmvoVgCgClACOqIEBmsFkkoXMIPnx93348wD+MwB/YXjvzwD4X0TkzxHRn7Gf/y2oZuM/ZK8/BBVy/UNf9gWunFxNLed4PDa59q53kBvopzhCbTtnztuOUJRCQIrmaRiTbz0r+3AyD2TbunCLax9wcyd1OKo+pakBYYBOgmjGJsaoKLydG0eAEhlOoM1j8rCbA/vqTP37fi4xBOUXDAtSTNA2pmDuem1qQSJiDWe8f2DpiD8NKsdEiplsKygEpMPczolSVAN4YRgugVFXKnIAK4SAsmr6sWRP9+5rCRoYah5bjF3Etf9uawK7IsYuZcK6rDifz/pMU0aIusL9uYpxPmqpyCSto5cMsXYp1YwuGjYk4qewzxbsCFoXgKO/50aBXbwF0JChFNScAdlAqFqNGlaQaLhIokahQKwQji01WcxT8A7p6klo1zEBygoR9xAsJEUBUMFCEKhBkKDVwpAZ2nesQCXypTXAeZfxTkZBRP46Ef3Mxdt/FMA/Zf/+rwH8NahR+KMA/oLoXfwVInpGRD8lIr/92PHd6voCcL2EMSXnaTkRwWKaC2FIaykuMJT8Wvcgz4/HlDCLLgJnJerOt/ncQa0V86ELgsQYIagG3PUuRtfX14gxqlwYOh4CmPpSmnauegjRXG7145wMJVUNWIUgJJVIj2lq3srYM0JBQq0zUJdbGY6NkQjdXbNlDmbPxEBBq1oqKChVWipwnCfsFJ1h/AC7z62bs2UyPM2n90ld3OBekAGU0s6kD38+o2S6N+dxbyqEiJzVI8j5gDS5chVwOp0Ud8jOwCymJmQtAW2B+r0ehxdp1VJwOp3UIPkpttDT06wjmIj273tzdZhfGP4OVl9SqxK2Am0I/BooZ0jdlCjn/SQJVq/u+MMKyAYYuQxMkKphhQhD6grICiCjil5vLUEbGPGs3gETCjPAEUwzhK1xD2eoZPHvDqbwrWGh/w6Ab9m/fxrAd4bPfdfee9QoiGg14uj+eh8HRdFLb9xiLiwHBcTWdTGPIiME75sQdrngbev0ZSf3dPIQt0nhO4Py+QtqDOBoBCUowpxSaqnRUguujleIMWIrGYGdR+GcBWXCEanHwQbiKahl1Y/magZmbOLpTxVWSTHpjpJz65ugmZczQiCUXJtL35zgVi2obnYjWNWCwHr9109u8Om3vok1Z7xZ7oBgBmWQqL/Mt/v9EltF2jTVmrH4jl0FQhWgATuxe+oGYZeq840gMrYNOJ3PqPUj9RRyhVQlsC2n08C2VCVpUDcERLB6COxwJvsHpAgWK42XBzbMfaizNxDjHB0xBDe4xQxkMVwIVcBUQbSB5A2k3kHquaUgiRV/gBBINGStdQGqNr5R3Ib1eKgAEqgWCFZAsoYCNCGEAyJNkHBACUeAJ4AOEJohmPT+sIUg8n7cgx8L0CgiQnQ/EfK2QUS/COAXAeDm+ogAMUXnAKkFW62IFIBcMXOE5A0oGSgZ2drLgVwFqXfo8ThYiqobb9vWugP5gl/XtbUn02azdhNLwXY+Y5onK+QBOEzIonLwiKzKNg5IBt09KwPMUVWUSwEnRfyzCCbW2v5qvIkIAor2tgwxYrUdkaoKjpNUUFZatkeBwoRqcHUtQOAJTAkx6CJftw2IagiRxXQOdEIH0u+TUsFJi5KeffwcH338CV7fvsbr5W7XzWjMiS/Wym3kGuiiYIvTlVhUyoaai4KlQqAwcBUMnKxmGJoidSm6IwohThNkXVFAKMKARBD0nGkrWN+8RlkX07bomhlEWgbB9mwMGeieg+hCLOsKLgJtDqBn5r/37AMNnmkPKdD+3RSrzShk3zhEjVEJAqOiIUhBkA2MAqkrqiwgKSbMoxqW2pZPw0AXDAaghpAGToiI4Q0EUAJjBtMRCDeQcIMajqg0o2IG4QggogYtlmMJYImQ96QjfR2j8H0PC0hl3T+z978H4O8dPvf32Hu7ISK/BOCXAOCbnz6XDqh1V9Yr8BRw2prL6cxA11LwXczdWxHRFFj7rk7ScTrvzc0Nbm9PuLm5sQYzG+ZpRoCKetzc3DR6czum0XV9CQUTSUXt2RPfDXPOjaADGNBHvuMG1exH3+FGQk2MEaHENsGBUYRUMZFkNRFoGZJifSo66WvMsPhkTimBje/x5s2tnpudb84ZhJHC3ElkDQuAegRshUhuaJVMxdYXgdo5OLdkpGGPeIU/l2ma7PzVg9DsUkWp/TnruaLdD2YVUfGFA9rjAf4dnuEQERAHiDjRC3DX4oFI4d6xfIwFbZ0QFQ1PqeCqWQMGa1rRPIJo3av8mIKLNgTQlGQzamRVrpwASiBOAF+D4xHE1wAdADoCSGbcrEkwaa7Tlb7ciXrX8XUYjf8jgD9h//4TAP6H4f1/iXT84wBevg1P0NErB31HUeBpM8JPb/I5suZGlNsn7phm9EXqtQ4KVqVGWvJd0XeHGEJLc3l87YQjDnu2HmBMNuKdVHoMvRTbF6aSeAK6BLiJcg2L1j/vxsEVgtodGq5ZgUMr4/b6A/v3fuGJxd61uevOyvzhix8q0Ur9ZSUDuYuOrkA8lnn7S4lSWbtnhdiqTQGY/oTiJyPxxw3TSKAaBV/8ezyr01iVVVBqUY3J0tv2revSmvk6A/bBmSXKqfB7t09Hd2D1bWMMG8b3GjgZAmLUCk3NirAKyjj+YF6s2Pm0IrRy//ur5RkKGAURwhMkPQGlT8DxW+D0bYC/AeFPUOk5gKcg3IDoAFCwjIVL/6lMHoHA72EV3jUl+RehoOKnRPRdAP8egD8H4L8lon8FwG8C+GP28b8CTUf+39CU5J/6suP7+YqoaArXCtopC6n23ShAMu6cPiGc5ur5517Y5EBkF0P1fDkgjVY79nQoOaOIYArHgU25tcnQiEtBiVbtPdKeiEfrYGX3zya5c/gF0SYNRMksjXps5xen1HoqGrNgNxH9+3Ri6f2rpWjjkybnJjDtWd1MrU7hdDoBKTUadtlyoy17mOCL3LkK7gl5BsgNIY27n01+re24WPjUm836dY3PM4aAHBjrtmI5n8EMDaWMm7CZIZqgmZa6bagAgrer83kk6n35tahxz81YUu0ZpLelHkfv6qHhx/N7hDDZPdlAJACrZqL6iNwMUC2j8drXp1RRXUdBBDCBaALiBOLnAN1AcICEGcIJoAmCaKlOAKRdr0SRy+4hiGGab3OFLsa7Zh/++CO/+oUHPisA/vV3PgP03a+aO7xtGw6Tcvxrs+r62dFbuNyRXd9Qjzl2Ywq2/tQQbNtqjLwC7YUYcTppJkH7OuTWTKNx/83Fn6wFnVOAN4vn+/XvJxdZtkH7VsDcVw2B1m1DqYKUugH0Xcz1Efy7RxKR764KtlUIrGms7UYjH6NKHSa4hjzDybXv8/s61kGMYcS4w3nGwZ53m/DeQyOlqXli7l5rJy3s9Bb8M8uymEEWLOcFt5ExTwmBgFrHc7DnykDdtGAKgTEFBoUADEZm9Bhr1UIoTgmSYc+9g4ruvQ1z+N4zvBxjkVcIjEreA7SApQC1IMiFTOtw3P69/RORAzIlzSjQwTIL10B4DsERoEkjBI6oCKgI2jLPtw2rMCZiyza4p2Biv+84PghG45iSFCivv+RNH/QAH7nYqMfN40O3A1n+15qwmGS7V/rpjh8R04SQEnjLAGl2goNqD5ZtbeCai4d015jaogu+++fSWrGVqtkGNnCNzBNYlgUhRFQrJQZR75gs0uo2fPJ77cK2be2am8Yg+jQiMxb+Xss6oLv/ZdtQiza1cQMYY4SEgCLGJFx7efplv0ugGzm/304Bd+9CpIN7CvxSk4XTk9PjkomgxJjuCdFouMfGS9AsE0fGlp2aXfs5mrHaSm6sxchqFJSU1D05vaeqDymZUPRGXRiF+3PSXX0P8y4NQy+Ec2Feq4nIGbVksPUlJTLOspDySjyNygSHDABVeSZmUDgihCuArkB0gPANKj8FkAAKkJBbaEAAuAGRSldXJMM9BXthNHlfPj4IowBYXEjAsq6Y5gMCtMhpSozlXCCcIGSMvFyQpqmhtrVWEAdskrFWRbGFE3iacD7fIaOqpLrX4iOAwgyhM7ayYd0yUgiQipaW892WOQy7ni9M27GJUIlU8p0ICAHCKpg6Hw44nc8a06+6uAtgCtC9/RsLIXHvUQEYgabUTm/Nqo9I6CrB1YgyrlQcvdsyA8X1FUQAKZB6BnMBEFCsw/YUJ21HlzM2C6dcdLZrSXS58BHLCQr/o5JgqRXVCURVF3aauGdFbLDVjoSY9N+0gVNEChGSCzYBWAg5F9zdLbqYjgGHqwMoTYjM5l4DkQgpTVC8fwBSOWjeHy52W1RoJwgSEdZaUFBRs5gRhbr6sMpa27krO3apnmsA9QwN6Wcd7WkcjSqAZNSygAywrSRgnoBajOm4AuYFckzq7lfpaUSeIfGoKUbcgOiISleo4dDuI0mCoQYI5KEbN81KD5thz0TqBg9A33V8IEbBkGjr9jSCTyRWY2+LzT0EMsrqSL9NSYkw05Qs5jNwsUmNSytvFtHYVAGrDclScI6IewaDeehqVCtKLjhea+3B4iIiIaBsFcHSgB7TA2jpsxEDIZvUTtdubE7PHAxZEl+YTaF5AP5yVsVq3X1bsk2597XagqF2TSqCQi3DQEzIq7eTM8Zi3peQ+//93GqtqhpkYQNR5xxceg3+98rYrGBB6+hFga2ycHjW9l1N8CUBxFH5JxzbQvUwcJqS7vw2fyADw9FCHaGOO52XM7aatciI9qHSJYgI7DUcx3kK6SX1PQTIEGyqQiULiBYEXjXWD8YuDBbgB1OvpQjio6YSzTMAX4F4gmC238d7KePd+dlzaOd28X8RGZCLdxsfhFEQaPmtcKeZMgfTO1SdhA1Aiqr2qxyCXgzkFl2fl6Lwsm3IufTWcjBXWzRnnq3k2KO+1dKU7o5rCq4LnHgLsjG+9gek2EeF5NxUkFxsVmpFHlqblVKQJjIRWRn0Frt4aIgRm1UPAn1BOjtzM2OUc0ZMsTE7tW2Y2ERR3keJSrNuC89YlcqORMs4jOItUvdsl/F6nfCloVJpyLr/zlWPhHoa0yXaBQRKAZwiQo67ZrQe1sQYVY1pXTEftJJVoJJ666Zitv43bOGC74oYFkPL+0PXoVj3sa1mJArqOUoHb3deGrAzipdGQVOyF8AkMkRWcKga9pNAaNMYTyrAgEiCgFDDDAoKJIocATqC6AbgKwAH9ZLY5AZZMQIfYybEsbYRgPZzbM9SNHz+iTMKBEXs0zy1CwzWaBZAi+NLLermm/XzmHbbNu1D4BLgtSKkDkYSB2RLLW55w/F4aCBmIuM4EONwOOD29rYtVi9fbnn/UHF9c91y1ICBl6Rt1FOYkaJWWJZ1A3OwlGE3IH6OoyqT75LjoiJ0S+8P172EOuAoMSbb+a1asSpxq1cGatmyX5MPD5NGALMBidQXR08Plt3kK1kbkzH3FnKAYQQpQTg2Ly5NEwqA1TQqYopIdcK0JU2YkXIvnjx50unjBIAYy1ZQhLBtSjrbjP8BWPg2THfNYqSWJdHJZSGB3beGUQ2ezHiPfaPpqWrsDEffd7tXU2vVsnRz3QNpdaLwDFjdAyhYd+0JFA7gcABoBuQAkQnCRxAfAGFUx53MFnldjp/L+FxIpLFLfVxmqIR+IjEFMqBOH9pYBFVKgdQCsaYmYiXJ27q2pqQu1w7oZjEKhgAwd1yzCldXV4jpgJevX9vnpRmhsZ+Cu/TwncOMUqBeAuxScQKTWOMA4r5g3bN0ZH8k/4homOEhy4j+j17F6Pp7mDQWFo2LV8yVF6PTBpOIG3eX0qo49wj7+H0jg9Ff/ky8YtMNbkoJFCPquqFYmJNihMQJk3l5sxmFzWpOHFhM04TNej5M09Q6R4uoklQRYM0Zt6cTDscrxPMZN01Jy+fNPjsQQ2wroBpfgtiJS9zi8Hu7/7DoL1OVFzN1t1P3960HJSVETGCItomrKtwagqBiBviAEK/AfERBBCRBwEpUIgLJprgFBatj8I7Z+4XusnRU0TzD0dANF6fX8+4ZyQ/DKDjIpd2jk7n5VpPOhOlwQBZq4hrnRZulPnv+zEpvs4UZOrFqLihR49Lz6YSnTz+CVO0rGTjg1asvICCkqO70mF7zxenufzTX29Np27qpEAl0UXsBkYim46ITkDDEpiEgxWQeDaM6GMaMXKtmMmpv7DKyKLdta8VIfp7btiLnFYKqsulJ71nBIMkeWFHwgf+AWhWhh6YqlaOxl8zHLk23D5fGEMInWYgJLLU1nY3WLi5DPZP5cEBMEVxKy/zEaUKRinAOWEVDxRAjKIRWX8IxYC0bzsuCu9MJ18uCw3lBKYIK78loMf0w4ddtbVwAES19J1EavG4WCmiO19gyX80dh2Yc2oH3BpSZ4JXI7X60JOBBcTBhUA1aCwJNpXJQb4BoBmjSdKuyjRoXBVQA1g5bjiewDArbok2E3bij7tOe90IJ4EsN3eX4IIwCMYOsOWjdThpDEWHNG6pVBqqClsq2p6sDJnOJfRGHGFCL8r0jMYgq1m3FlCZcH67w5s1rFNEORdt6xjzPqFvRUtsqA3gmDdwjZrDyUBo7MoSgdQRmOEpRHIFDgBR1FcOsQFK1VBFxUDou6785KNiUWZADkJLG6BxVeJUugMcYY2t7p9d1QpUFuk1UxKQYBNjZkASiCiKl/gqgOxYLOEQwqzaDoC9yF619KIYeQ5xaKyr3RjoxJlTHTzQ9gjroFqhyVgQtjHk66I4/H7CVLkdPBM18MIECA8JgTTXhvJxxSieUZ9pAuBRgi5N6f8sJkrRvJTOjQLT4bQBK23WwPpPoBUnY4wb+f4LCAHoM/duKvtj8sx7LuycWbOFVTFqtKAksd0BkAAkFAcwHiFZEoNSsIQdN8M4fBILQlep7UjBlJ4FkB401BJKcNWwYaPj+HMewB0ADnEE/YUbBsVFH0H1RehyrqJe0OHGaJm3skTuD0V1qR7O1VFjl24Gec3byjEqgY/egx4IdIk1DcTDuOhGWdUG0KeV/52h6CIyUptaeDHXYrUSaEWHWPgyRNK1ovvgOMLp8fCO5JmfdPYGenoR5Hf7gu4T6EFoYBhPg/I5smoGwv/GFzztqtL/cILj0/niPvCs3uLP8NIzpjWaYGWEK7RkDaEzOQIwYuHV6ijE2ANTDJdeg5BBxc32DIBWvb18BwkgH9XDEFnP/zoAq2vVLz9e9ALRzHM9vzA71+26PCKMHJQh0X+TVVbz1AUYUfgJGgGr6BQgiSrV28VIhQnZsahqhwNjY1/AjE+Udw7n93OhZuDGLA6gxVBn+n0Dyku9Uvniur69RisXjgXA4zEqBdtfecQgLNaK7107oYeDmeI1q/Red1OMqPsy860AMoKk89QwIt3SgYhuKynvcm1LClnPDBjR12fPbjZBkNQ+tp4JlWYR0Ya/rimOabeEpJ2GUmddz6crHary4Ie8AbOvqu1cIKvOxT5uhwWQC7HaPBkSW7qrWYVGPZCA/Zi9gs2z5UF7dAMmiGoXaw0LVhxzMPF5doW4ZbIvCe0f4dzq+siyL3g8QDscrfPTsY2BbcfvyRyiiwiyaBq3NuOrfE0hUlGZUwZKLBTJiLv7z+H92L2iYsUGVbmwH1+vMeQOogKiiCqFi1usGgymgmkGoxCag0rtcheCYAtp99pCvmoEc76uf3whSj/8fnyupGs+XrMI+Pgij4MMX1iWl1/P/IWifSKUHa1Wg032nacKWNxyiItNEhClNWLKh1tu6c7EcmfccvYch/p2X7qdz9WNM2EqXgIshNCUnQEFNfVhsRoaR107iIWtnF2PEmhWojFG5FUspDQAcEf8uQmolu7UghGQTpzRDIcyNMu0ek3s5viu39O2FJ9MFmW1uAAAgAElEQVQn25DOGwybGwYPZdyIC9Davo/YjK4/w1ZAiEFLeNd1RYEqa1FKePP6NVSTVLfx8Rk538M9hhATrq5vEKcJaUqY5gPO653Ru/uuPwKBzIQYCBv3YrMxvBiNwWU6sv3bDLgPcbyBYKQhgXcyh5GhqgCVg95/+3tyj8X6X5JlKoBun2Ug5KlRKE3x6zHDNfIpRpLZDkD9STQKjZZbev8GlXlX94mpu+uAI/jSQEBixrJmIFq5MnHTNxjjQbe4yUBHADvEf4yhuS3wYKIqI3Fmwheff47ZRFRFetv6eZqxntTFVwOgmYJ5nneVkDGmJjLr7u6Ytx8XiC9Wb3UXY8CWO4lG02K9RFw7Q+m99YXAbOIdIk0233/fPAORe7vSWPjDzLsq02BGuXW/ssmuYFjpSD8Jai7YSsZ8fcT19TVyXlWWPkQDPvvEd/7GWCuhYKQ26qXIiDEh1tBax6nOpougGI4A92r2ab1xZx03gwfTkraoR+9pdOd1LiqVnsSaEYF0znJt/49IsDJGFYoJERUa6mphWwWV7hXosYsWRD+AFzx0PT63RsLcZcjxZeODMQreKyCXgmNKWLcVMTJcxixNCbe3tyhbRrR2YAzVWVy3DLZek2veAGMJRmYUo6jGmLQZbGuKotJluqHRbmLsKgRrxzIYWvxSVjMwZmiKN3ERwbptmNPkpWkDT4EbKs5OehdrYcbelaoDSq4ypSQdRdu1K5Lx3CmByCrxLGSq5Dx+AohR4TuHCv94/YirPGsth/Wg9JhC9gzMRhQyQ+VFZ7tYeujIFTkgECOTcQIM9Q8QLCWjcMLN0+c4HA+4fbMqpyLEho8QaQm7BBWEPRyvFBS209vWBee7O2BKCNMRKAuyiCkJsAmeugEgJQ6ZsdJig9q0LHch0MUuPBqFptlgN1JEsJXe3Mc5L9rCjkHCKuZCAME8E7gnQkZM0pCjVG7eWKlFMw1j6AZoDRCR8hwcb+hX2ObwyLdojE6RVhT3ruPDMApEiOmALS+oUrHlFceD8r2DtZEjUuGKQIQ5RARiLMsZIgBPE2JK2PKGJWcIAZMI1rtzawqjC18Zcrojh5ZXdzd91CVs4GMuKhUHaOrNbq6nLMlwDCdXFaj+f7EGsLoa1X2u5mrGudfWSyk64XXVdm8F2kqPmRCnCMSAvK62GxGmcIVMApBei1jajYnVdQVbJR1QQQBp01m276hiDE1Lh+lmvp9MQAdU3TCMIrohBDNUBLLGsjEk7UEArY2oIgAHkBRUZszPvoknn/4UDljwxcsXiEyYSb3C82CAiBmICWwFVDFFbHVDWc+Q5QSaD5hunmElQSVVqCYhVNZGOlUEyBklb6YlGfQca20FQ5du972wAQMpyEMu+/86SP0LBGDlhDhK3BZqBYoQIBHZQdbqnkZBrut+Vx9AUJ1faiAV16AuRt3iDQGh2tzap5Cds+JY07uOD8MoDCkit3gpRZxO5x3wt64r1rQ2MM13DykVEnrVnT8sR7B9sXsLds8yjEwxv5Fj3j6EAFm7hoL3oXBgcdwtLyeZN1dNKWK1h+6kJ6DTox10C6SNYmm4D57ucnCslKyLftzZRFpY41mRLsiKFj+zcQBqrc0ddSMwot1OoQa6Oz2Ci/tMDyGIte6z0KLVFDSX3jIUBIQU8Mmnn+LZ049Rbj/H+fZOn4N3E7+4hyqVp9gJE5lClharzfOMOE1Y1ltsuUCFRVS3wVmj51qR4SpRGkIozrs3dKNhAO6zA8fQ0r0CBzT9Pl16G+Oc0meubf78HrbdvBbDAKVjFbs5MFQHP7DbU/MEHvrOrzY+CKPgrpVs5mpbI5eRh8CsisyAy8EDTr5x+XJPZRGZ/HboXajHWBDoYcLYkNU/53HsSC/2ONX/tk0qn8ignZV20tGIQ/gEF9nXOvhxKPQFDzeQMAVj66hM1K9Ff4+BvtvrJPR8u9EaMzs84Cs5Z0wxtf4QtRSIgYj3ntOwe44TL7C67XTxikGVmUIIyDUjHmZc33yEeTriRz/8Praz6ijAcIlxkXII4KRg7rquuDudkLMZoqgy8lIdhwlw7YAw0N3P53M3wnBBXwJJbaIvfl2jpzCCcyMVvGMKGqJcXq8bFR/j342bx4jfuPsv5sU1Y3qBd/jz8jECw3Kh4PR1DALwDnJsRPTLRPQZEf3t4b3/kIj+LyL6W0T0l4nomb3/M0R0IqK/aa//4l1OwheHHWPQ4+uWeDQOzbWynYGpA4P+u1I7wHhpyRt33tq0A2iIuuIOPduhO62eYxoWSwOk4JOolxg7wOZexwjoQaTpTrZJJB35xuAWKrgoLezwnQa2s7RJBxhXYp9i9XvY6Mno6dJaa8veeCESAGzmXY3PY5z0l262Z39qux+dKkzcF55wxM3TTxGnA07nE1598XmjNWfUlkd/yKXPOePu7g6dzk4GuALa19PJRGYgqO/wGGJqB2Bdceqh6/HqU5ekd29zz+js93RMpV96Cn7/dp5B6dwaTRfCyqC5leB7mDZ6yeNaGV+erRpfu3MgdLD3Hce7aDT+eQB/+OK9vwrg50Tk9wP4uwD+7PC7XxORn7fXn37nM4Ei9XW4gWNJcYihue6tjbyRf7wRS+MfxIjD4dBQcs/5jxM9GR3XdxZvPDM+2Dbhq1UcloLbu7s2YXyRw2PBIcccoz7QkYtfSmkkGNds8N3e3VFn57UScnKNP5skIZi8fdc9GLMV/vM4SVLUnhJ1CF18BwR1g6RcjIHKLNKyNyPYeJkbH3dBItN6KAWBg6ZICTgcnuDZxz8FAePFi8/w+vUPQdVCD6KWfWieU+i1KDoI67qpBkQpDdhLcULgCEZooUsttUnKN+UuSLuvl/Ugfg0eWmoKe2thku/Q42K9fO0Mv43Ro/R5MWYC1KOlXfuChwzNpWEYBxHuXctjr3cdX2oUROSvA/jRxXv/s4h4M4VfgSo2f/VBQIiMKllTMExNE4Bh6kHZex9yK332TjuuB+heQzQ3cexj4JPNd8F1XRuxCDYpnCfhu6uDQYBYpaFWBwJomQEAmA8Hpd3mjMSau3cEPMSgwh6eWSDN6wu6MvPIl4C7iSJqUMgFQPQcYtDzUBBbd75q2YN7O5IIEALCpGq/UkVjb7ABoKI/ERkdFgpsgVCqYKsV+WLB30uLucsN9VYqafRMHJDmGQgJBYyrj57j+skz1LrhxQ9/B6UqUKmMQ8NDPNQiQrDnyga2zvOEvC1YlhPythq1XCswQ1DBUvcQ8rZh89b23oRlIDVVY3cW0bL2PHgHqzUauly8O09p8DAei/Uvx0NhhIZO5CsbuChDHw3W2xbPeG48hGOeHibmtxqWy/Hun3x8/MsA/qfh599LRP8HEf2vRPRPPPZHRPSLRPSrRPSrd3cn5LLANWypFk1tmVulD1ZR91IqwAHnnLFJBVgnxrZtpqxMiKQEqNvb2yatNk1Tu2m+i4QYDIAjZCkNpXUqr56o1ktwCChZi1Ec0HMvgXxRZW2aOs0zCqMvGBJUViUezUn0Seo7FFs45K3wWukvRjVigCgC4qlH6edB2E264hTqoNV26o0Q2IyDmKpTGJj3HFXHEMHa1kE7GXi4MQKObaG0yc0QZk0lBs0cIMxYa0CmA24++Sbmq4Tl9guc37xGjDNo0joMyStq2Rr+AlIMgJnAQY3DNEXUmrEst9qeflshRAjsvBXXQJbm4UitrUaAYWxBUcXkLBW5VmylYDXPz5WkL8PNh8KMcVwu9kfmezvWGOaptiRb4xb3ZPbHuPTEdkbgwiCwGxo7pjA1zO1dx9cCGono34FuDP+NvfXbAP4+EXlBRP8YgP+eiP5REXl1+bcy9n345ifiblrHDQa9wuHCNc1Stc2a3dxGMfYQgTRTMcq8j0BiLyLR9mUNS7BYsS/IDvDUooIio8vugJbvLEKCaE1ZZCDjsLmGu4VEtDufcDjCJfsapdhkvqqDneIxb+mLHg1iwJa3rikxLF7HH2ot8HYoSh/nVjNRjbMfQt9VmFkb3mJfB3EZt44TPXDQWog4gWJCAeGT55/gk0+/gQrg5ecvNFkaErJ5fsQBdau9HHzYKV2dms3tP58XvHlzi+PdHebDDTzTsuUCZkG0sGc8Pzcwe7BwQOrNoxhHF/29j62oYWnz+J638BCu4J8dQ43dOV64+Zfegb9/+X8WFaUbx6WkyvsYBOBrGAUi+pPQTtS/IHYFIrIAWOzf/zsR/RqA3wfgV7/seNkbgQCN7+67u0OyPjmA/rBGVd3xRrpBcEPihsPOHQCay+4v//Px4QTWnXXLG6aQmmJyTPpv73ngx2LW0KCUikpahenHG9mMvrCai8q6k+mk6/L0DRwz0LOUjMt+Bbrs+zHHSar/7n0sEvfOQ0za3apaWOMu7HgvY7K2ZcPxHkLB+6SGhjJhgoQZiAd8/K3fg+unz/HFix/g9osXmKLiPhAlktUiyOa5EPX0aXsG9nxKLTidT3jz5g0+OltRmD9jy+rcA9qA5p47ttCMgV9LrbvKyUvP4PL/eI81Ni7y3W7+wHEvcYmHvI975zSczmWG432NgY+vFD4Q0R8G8G8C+CMicje8/w3SVjUgop+Fdp7+9Xc6EQ79YdWeMdDdIzSuwdjGzME19wRGWXGgF8F4rwL9nj29dXxIvuN5JmK3EIrWOXifh60WrEWJUjH03oluEMQq5mhw69v3GJjk5zvcv0aH1tiVDGC1yVLFQhtPf2p6jEPYYRz9Oi8QcYs0WtrSGI3+X7a+iH4ulxV3Ph5yl8ddmJjB0xE0XeHq6cf45Ns/jfnqCT7//AXy+RaRRAlPxIhh6sZ4cHM9M+FenV/A7Zs3ePXqFXLeTG8zqiEy7GV8ZmRhCFEHdxVn6qHQeB3jfHgMqKPRwDyCJ4xG/7H5dnlfv/Q7gYefBd5e2/BVDMOXegr0cCOYPwtgBvBX7Ut/xTIN/ySAf5+INqiH9adF5EcPHnj3JcA0JeTN46Se9/eFkq2wKaWEvG2Yr44gol1lowueAjqpaHBF3Wg4R8D5CeOu566zG6DAwXbmhDSpl7KsK+JhtiaGRmMedm4ias1QaqnA1PkAbrAcP7h0UUfDxSGAsypAh6A/l+JsQz1fP5ZLr407RDDA83IysnsithsXy/1XKE1bjXJVnQlAy3Yfnxvt3jrmUErBzUdPEJ99G3drxbOPv4Hrj55j2Qq+993vgqWoxAAIHFSZmMAQKbvdu6p7syvI2raMly9f4fjkNc7nRc+d9yXPoaUk9Ry9ipFJMxzOTSFCz/CAEIkaVwPQz+PeYhQ8svYeBPJG72DHH6EOIF6+/9gC50Gn0fGjdx3M72cYvtQoyMONYP6rRz77lwD8pfc6Axs5b9jWDIg2BSm1aNNQAnIgFCFQijivS9vF/ca0kuJqkt4gSAy4W5e+400JmQJOpwUImqa84dCyEI4RBA64vb3FJ598ompQCBBRI3Iqtyh5w1WasVSbaARkVKR5Uo/BgMgoWq+htQ1JXeStWFhTQVyaoYhJu1YrKLahrKuKlNYCBiNxRETAabsDUMBBa/FzqdiCMvSYGKlkjamjxtWxlU7DwMsNhStinE1inMB1RQQhglRSvhYAqtLMrJmKKobXBFVICrVa017orklAloIoGcdnP4Xf8/v+AD799t+Pv/vd7+Bn/8Dvx5wIf+d/+2t48Rv/J+bDEasETFIQaMX5zWtdNHlB2U6oBJyWBRXAMTzT0Ec023M4TFi2DbdvXuGLFz/Aj55/jGk6YFsAFkagai0WBIEqYl0hIH2vALDahGNIprGg614NT4/5q4nijAtWL3UIBWzePlRo1DYINm4JCCH4wnQPDwAYHCbzdCxjQACTG4v++dFgVcsUiQHM98MZVrEaG9R4ku82PhhG4whwgdBEJ7L1ERTReoOSC6aoLbourapI5+S7J9DUkqo08ksDrngfy2kW4CKug3YUKlUVjgg9LUnMyMsCSaqwrLu2CnTuQaNOJY4xwst8XR8C9lmGypshb/An3fPkVlQTeiosBG1gk6YJWYBctVMUO+nKiE8ep4sAsErKECPAhPNZdi46B83UeNmynlcngQH7xeLeR6CINN3geHyKRBHr3RnfeP5NfOPZN/Hmh7+D737ne3puRJZ+VMB0WddWXdrdbm79IV0dO8YIUECMjMN8wNOPbkA1o24LmKr2s7CQCHCMY4jdqZOpIvnzMaK8AyG4j5m0v0enfY/z4zEPQf/OP/vQjEc3AkPocBlqyoN/vzdQ/Xp96V+ck7wXDPJjSUl+/UF+8feJHRo/90WoBI++cB2QK6U2PGDbNpzP5+Yl7NB0wwvGMZJKRvAxhmBVdyoCQjFgmuc2URuaTJqSTCYRVwZW4OjajgZsBEdrLbs+BLncz344HtDdyA4kObFLr9fvzei+6neVktHrKfbXvYutB2q4h11jmDAuDGZl4hFpg500HbGcz/it73wHT6+f4piucPvqNV5+/gKRWcFUAWpWY+9ZlNEwl9IxIELP+ACqkXF9dQXJGdvpDtv5FrV4tAp4FWzzmFtcr0cbQ00RaO1BvZ9deRv6/+5xOqMRKIaXHlr//Ta8wK9//N19YNe+g1ifAe1xIF9Xe2zm7eOD8BRErKeDjTZBLBY/TlcoxYqFgvIM4jS1Sak7RI9xAbTUov88TZot8HTjsiy4mRN8cY03cqx7ICYsOStDr3LrRdFBNTNcbtBEOnHpgunmCzxQ72/guyFxBosgiouKaM9GH6NXM2Zk/NhloADD3MpaBTEp+3FHDDJvxfkaO6MlXX0aw7dc7kyjsVPyUYEEBseI5fQGr1/dgmrBcneH3/rOb7Q2agw0ifqzqzsH07eEGv5aOm18xC2YIwBGzRm3r16CpWI+HHC4vgElFXEBLvCAdu4GOLaF2QuSqlWyumdZ+f7iH9H8x8DX/SD0Pfeh+J9wCQQ/hidcbih7A+XdoahzX3AhoGPg/buOD8IoENRil9Lr8scFXox1VqWqoKc9k1FM1JFrB/qcpLSuK2KKmKxhrRuRaZqM+toLfzwDMoJdHPXza96U+Va0s/MmnQBFXrJsZBk2NHxcXB6Ta0l0727t37PrrVCrlXWnwXOypi7G8tPz9Z97qlPVfzS92TpJw1mOXYJ9EU1vjupO/t2+AFr/ifJwqq/tVLKh1gUUCBUFb159jts3d/jhD34bFAO+991fx2GOCOIFXFp3UUrB8XjENM3Y1jLQp6HMS/eEnK0atDx5XVZ88eIF7l69wqff+gam+aC9JtqZDYaeqNWhMAdr4ab3SfUM1CgQunT9peHznx/CD/rcuXS6FUDVs3nQ/wfT/bDhoczOePxLoyG2Hog8s6SuWJ/DFSg/ocpLDBXv3BjI26Z1+dOEcjqjbrXJh1VUpDihlKz9FgOai5usCUs+L6hZCSlTjEgUsC2LUm9NFdnFPFucSPbwSHURYoqWHjxgWxYtyQ0RC/Fusuju2ydEzhlTCIiWOy856/83BRJ1UZQd6l9FMFGwhiJAMfYdiVOivcCLTecwwNuLs4VTTv8GYABj79jtdG8xL0YAoHj/Q+csKHKfay+Y0rBB+zvuCrhskMXEuQLCESkA2O5wevU5TmvF93/r1/HFi+/h9OpHmI8zIgKW00kb6tZsPTQYIRHUgVOBFQTCVmqDx4hZWZYimKaEmBJu794gp4iyPQEhgxwcrQQvkAabcjKpNkUga/IqZehDKtYnQ7EMsrDicjzkKbW5O9yXvpAJEMc0Hj/eo2HDW97fGw1qzV6UgGYGwTc3wXt1nAY+GKNAkKKTk0SbuoaQVFEnqqBKrrl3E/ZUGgCUCmZdsCUX5HXDFCIKbSgA5jQhxYj1bgWCGLCjwNIIKqWYer2DaI+IXEujAytrWf+OmEBVd7AtZ8yBWw7cnUYijZ8DqUw8RJqHwN4xOKhKD0R7BRdhpHnCsiz6ncYOLFWl1mCdozF4Sy22hE4MJkaESrE1EVDzRNS1lJarhygy7ilgsr/3NF+tte92D8TabZdDAKUDAgT5/AZlPSNvhDdffIYlMOY04dNvfwO1ZLz8/mdYT3dgAlIIFmlpNWNwhqUQCCZoYt8lpEKvn376TRxSwBefv1CMxGRkLHo2Y+rnZXUODIRqt6xWuCy/hw27VCF7T4mHQUQfl7v5Q56UP4CHl7e6/H47HwoP3vbdbVOCCeXI2M5Q2u8AKPD91iPuxwdhFAgGMhZdAFdz0gdHPTanbV8tFmNENpZbCqmROHx4luFgGopAt7LJmqcsy9Ko0u5teJpQbMGITdZqO783LfFGK44leMMWIi3W2uMSKgqyi9/RKxgJ3jwGxpEoagyZsawbYrUmu9CKTQBN2g2AhV0dDXc8RcupNU26Q7RFyVhjsZgep1O4/Wd3cS8Rcv87vxaCZofevLnF+XxWqbxzhBxu8A/+Iz+Hn/69P4Pv/T+/iS9+8AVqvUVgQmFGKRuIZPe9RB3pz6WgQnBMCc+fPcfxeICUbOXnxYyku+jj1NdsA4vRyuGiNqrG5PfB58VoXC+NwWVI8da5PGAEna7+6KdB9X4WYX8c3f0vQ5dG1YbXp+zPYUfCgmMp7zY+CKPQbBoB0dJsIQTlHeQMmg8au1caVJUj6m1XSiqlgHynN3qwG4DT6aTuMOnlNiS9FERbQGNVnJdcA51spGnNou48oXV24hhwOBzwcrDuRHqeLtQKdKReaw6Mtps3MwZ9IjjIePC2deL71lhDb30cLCvhM8KBzZGtV6VjF+N1Xwqljgvdj9FSoQ4o8h4Y8wzR/0vd24Xatm3pQV/rP2PMOdfaP+fee27de6sqVoSKWBEpkyABUUR80ZdSH/x5MFGCMRBRIS8afBBDwAcTQYSAEjEBjQYiGiQgGgR9MAmowZCkKvdWWT/31jn7Z/3stdacc/z03psPrbXe+5x77XPWuXUN+w5YZ68z15xjjtFH76239rWvfc1wnFqunLN6QYyf/nt+O/7+f/B3Iex2uL19h/vDEaOTsKOUou3qJdw6can195wS/BhxcXGBcbNBWhNKWpFywovLSwzjKIIrGgbZWLfsjAJ6rhnDL+QfdJvEYwbgsRRgD7ra3+waviiTASbwlyQAzej2392fszcVtiF4f1p4ReR//IwCgLpQEllPyTY5WtqxtVMrucmOW+4+pSSVdroorMdDVeNxhHEYAVgz0lDz4f3R1/FX17wU6ZHIgmvkIhPf0nemewCWhjOm+mPnNmGXaZqEl+E8UjpiTkmuiVqMXncIO8g0Fyzb0rmr1ABX+zFad8m5Sq4BWu5tbmdpGgEfipdtUQHvN1ztdyPo+FiHbwaQUsGzi0t856d+Sgqe0oxf/e4v4fbqCt/++jNk1TyQNLRoD/bpN3su5Ft/jsNhj5IZy3TEdrPBy5cvxdNyoktp49E54iASDkw/DqTPqu/mbPdtI/8hj+D9mP7U0+jOhj6sOTuL/vu08OQxoyKvS80G6fjYvA2h6WnKlfjuO7/8+CiMgk1kSyH22om9GhJzE1IZhgGTuujeST+I4zwDigcsy3LiMscY4WKA9UkQKfmm02hAnFNXX6omXTU887LiYrcFA1gLS5mx6iKstbTZAVSq7Fo/afpiKOpc8qiAJieTnlNxEe9rf0nTFgSUnkwmT3fOqW+UamO91TF20lzVJnyxNBZOJ/j7OgLy+X6S9btQdVO73bUURnYDnr/8GgoD9+9u8Ru/9l384l//axghQOuqGEwPjBVmqfrrDLJzouZ0OB5Q9jOWZcXldkSIW+kYZRyUk4XZjBmRFlilU7GUx+oP+nE4f+08JDj/Ww2lqmHt4vzzRW0AKgFWtWqHnJ9qSGU8A0D7f3K7Ruv47bTTdoxBsLHQxk+eh0P+cUtJArYLBqxZOuiYKrLF9t45uHFssVQHfJlQiYmpQhcHAU2QpRQMyooDKXFF+RHrugJemqMu2gV6noVO7QlCNyYCSJrDmoQWEUm6Tnv6cQRcEPS8gJtyclGx2CEio2A7iMKUD0HoLc4hlbnzBNQYZQlRxDsR4RMqCeAMIaswvIKW8A4IHmtOomxM2tWICshrYRWEGu0UxXBgZJbzuqAt0CFxal34IYCdZn+8NpNxVH9kL+SqY0Aa5V5eXmLcjbi7e4sf/Oqv4Lu/+DdA+QgXR8zrVL0Zp8Qn4QxAOqwRgbyQosqakacFMx0wL4t0B99FzA93mFzBur7AWIqQtsjwAAJpmza5TKeaMgqnOi86A6a+zG1nLl126Xx+Al2oAZkPzYvTTAAAa8jELHiCYVNtt1Zjyy00sMNEfUACAIsOhBoBlnUB13QUQhwQ4oAYJCsjhDuum0JWNm5KP2bkJQKpFLUDuYDMUpxjXXlCCFhn7b2gO1K/85ZSsK6pk/ZSJl8csC6LNAUhEWiRzk4MH6TIac0ZqRShCncMPzuGEFBSEl6DTh5zwU1222JZ55UxOQwo+wOCk9QnAbIjwpSiPOZJ0pwZ1nykVIMAiFK1ZFcEF/BBZM28rkco8Amog+q0DkMZgoKJ2HWJsaLMSMwoVdxWyS8ysEAIgt47Ef+wTtLZQDwvsTllV//Gig1kpXlzkdTy8+eXAGe8+uw38Nmv/BrS4QEgYM0rco6AelLeeTiKSLzW3hR2/QSAtRAuOA/vGNsxgErCNM3wVLAsa4ud9bNyTyQpQTKQUR6TIyeZQiKR/gdqoxfUEfuCuVq9v7NmvGSqBrrQiYT7UJ9k72E0b+Y9AFG7gTtyYNLsCJqH6T2dVNmGOIoiV5AQQbgpAj6nlLAsa+2Q9tTjozAKzIxxHHE4HKpEdwge8zLXh1SR3M4tB1DBOEA8Buk52FzPYRzBxDgcZxQGtttt7dnAaLoL3jlM04SANkFMK8+kukivzXZ0e59hFmagcpbqvVwKonkUrGXU4MpRcM5J0xuvsuwMbbgSGjDojQuvu1yHJzCfMtUM3GoHKamraT1mDausqUuGZDqM50GFTwRh+rEXb8mDfSsg68vVLZmlcukAACAASURBVNzYbDbY7bYoKeHtZ59L8xaw7FauKXQ38dQWpgCWMha3eu3k5EOMQuhixvE4iValpB4Ej3Dvw2k1zdhpQDLZorVRejxs+NAh9SrvsxHPvlmur6B6Ce8DhVTTn/XcZLkC5VvoPCUnhpiCr8/Oe48Qh4qNpJQwzzPSumCeZ0zTjHmeBFvr1cS+5PgojEIfi8VhqNz/Uoo0YiFZvOM4YpomAKhNZsVrkNuo7dvGEcvhWHevRQU5x02o+IURc+oCkyupqH7uMhh9s1fnXIvZ9W/WOcnuw86/Ho4o9no5BY58CAJYaml2zkWk1rsFVnLRUmBXY2sAtQGNoP7acsx4F11q1jQJABUq0XG067DXHTXR0FKSdqeWwyZwn6XovbU+2yHYScAwDFimI+bjEevDAwKJPFcpRdu6S/v49IFt2TAT685lWZbqDcC6UrHCeJ273ZWYFy7S0t2d1aDASAvvA3kfAvY+NG9PcYT+PLK0z7NBHzpfuz6tXRD3DYBJ2ntNh58aYyJGLuINHA4HHA8HrMcDpmnCdJxwnI6Y50Wa3z7x+CiMgk12yfuLe2tCGAZi9Q+glIJCKtxJwlEAtzy3lUCzM0KHTVgZcPMOgo8osdR0VgwBeW2l1IA2oFnE9bLXa28J10nE6UNt5/Y4JmkAUu9SXUBTY26AkVOXty0CSbUWwAXdEa1YyMaFEJwZw3Tihtq91thYFzBKYyaK6+6Qar5fPlM6QRo9Gz6wJgCgFp+15jeC0cx3t0jTApcSnLINQ/CIvrWeS8vaDODZLiqGmVv9Cilwp8/6ZD7kvtoRlctx/swtk2SexVOPfhHL9clXne/8J4u+hxDOjvO0Zj8PgDbXJb3oEIagXmuoBC9Lw8/LjON0xOGwx36/x/7hAenhoWpOmofwVTQVniKy8l9AZNdeM/M/oK/9+wD+NQBv9G1/lJn/kv7t3wXwBwBkAP8mM/9PT7mQeRZ5rRi8Coto/zwiyU13YUMpBS60VCVgBVAtTWm7c1EQqpdmM93HnBP2+z12u524pyGgpPXke2xS9tkR2y2t5ZyVadtnjHMuAJTuAq41sbWFI+Ml6kEheOSlSdLJIhbhUhBVOq7JgTOgpcjKKTAiUpfZkGvFSQcpMwq284jWtO60fTahm7CPpe7MI2JmHI/H6lmVIs1bjscjQmYMLK42e4cYB4xdj4nSeTJ2/c27ITVY7ZnWngnm4blTIph91krVrYbGvqs65p3C02Pu/2N07v7385TluVGwUNHp/ZyfVzAmvRonal7mVQCmJu4QQ4SLHiG2WiBmkQZc5gXTNOE4dwZhf8B0PADTAdA5GtWzGLUN41OOp3gK/yWA/xTAnz17/T9m5v+of4GIfg7AvwjgdwL4DoD/hYh+BzN/KfS52ewAFQXJTqr+SKm4LjiMMYJzETc3BLghIi0zsrqROSWUlEFBcIRUEnJJKCXJwEtgIJLiXkpw05oQ41Bzu0yMDKGNwkuHZugCBKAy6HTiLeRSkCGtx0nl6QmQbkbeOie5mrZ2BQhuQAKD84KoMu3sCJkYGaVK1xcPZC4IzIh631lj5BCCAH9e9Qm1itT4DoBiAbnAK/C2rgnQXgvOe0Cbv1rHpEAOid/fySS11xF6FLUzj86+m0iyLktOWKcJFCIWFGRmeArYxEHk9x0hdalTQFWfIDsJFcmuZC61dF08yAUhOVDxmplxAuaai46mAEXiv8t16a7KRMoQ7z00Oikrtnt/vJ4B1as8xwjsfX1KMgOC8jID3qT6UfEIkdf3tS29ZM7Ek5Ru5UEL7pymxRPKuuJ4PGKaJhwOBxyOd9gf3uF4nLCuC0ouiABC9BiGUbzmIWKz/REaBWb+34joZ554vl8A8N+wCLj+v0T0PQD/MID/44s/RjV1KK4kqgvunfRoIEixjAimChIuhSDywGKM0hRGTqfEIhnMoLl+Ke7RcmXvsSyrxp8Jw7DFfr8Hw+oeCgKkN4Bp8+eU4YbGUDQ3N44jwhCR5gmpZGzGjU48rg/V2sx78tBcIfr+C6kU+CGgkKRRwZ16ou5yMlRU4/+EtkOJF+He28FKkrQmuHW8AhpYBlJMQhe6na+x4xwkeuoqBlmEZtZFvR2gfre4rIukYTMh6bPZjAPmBQA3D2xNK6LhKFxEZh/CpyBHcPBa4Tpg2IxY1gnrumoBlShTB6OJn+EANYvErbeFGK1Sx+Cx4zwd+SE84TEc4b2/EdUMlYUtrc+n9iih1kyGyIGCq0ZfwkbCPEsIO01HrMcj9g8P2B/2mKcZS9pjXvYyN73HZozYhi02mxExDojeg4ODG+J71/mh47eCKfwbRPT7IErNf4SZbwD8JKQ5jB3f19feO4joDwL4gwDw/PnlCfXUOaodm9aaz9aOxxp3W80CmGuJsIFNgNClc0eCyTkjwMRNJE6epgk5rxhHAfqMLASgxeH6+V6WPZcmxOKcq7oIAGmIQWBurcXsHOJZCF/fJiyXxs4kyITqRWOBLvNS+0PotXTqxTY+5ylVQLGIkuHM4KDXZWjqTv0u673XMOsR91g/07fYM6GZlDKmo3T79hsxiLvdDjFG6a+QVrgkhp6LgMIARI5OgIOKd7A2ezGi2OAGHdOAYdwgjiPIhw4k1gxLxkm40c8tzvyV4usPHV9kFPRy7D+w9vFELSsmJD2l9NuGoUCiDjrSKpmX23fvMB2POBwOWA57HI4HaWGQMogygovYXOwQg3Q62/gR4zioXL5Ddozy+FU+evywRuFPAfhjkPnxxwD8CUhTmCcf3PV9+Na3PuWcM4ZhqCIopsJkyK2hz3lNoG0DmSra7z2m4xFxOypTThewnrcHc6z+YLPZIKWmyWAPSq+vZjxWnfyWakRhod9qKnKIA47q7sprA9b1eKKR0DPeBIdQebfaCck1/oCGLBUUBDTDoBPLYv/OIFj6dJqmhnbDJMo8wDhRtba41e7dPlMnrBo0U73qDYMtuGVZBIfR+8lrwpJWLNNcqedWR9LjAibnb1kbMBB8w3IqUOo8nCp5hxhAPspkJ4fd7gLPX3wCH6MYBvKSLu3CH7uvWjZeF+VZOID3sYUPLfYGKresgh0nOIXEUyevnRgE1XfwIdbUrG0gNrbzPGNdF1y/fYPD8YDpcACva8WwgpKXxjFiHEbEQeZicK1PJjNLmPilDIx2/FBGgZlfdQPxnwP4H/V/fwDgp7u3/pS+9qVHKaUODrg1+gwhYE0J0YW6E1lMn1MGozV+3Ww2FYQqZ4sRaAKvVqxkO6UYolGyD7m895kYI1JhlJQrAp5SEteUTrUeydHJ4rLUnY5VTS32YKUZi9rfIGcMMVZlUefFjY4hoKyKF+SMgvLePVpDFTtyzojeI+UEzrlyEIxR6X2qE917oYvbzl4NmAG3bDH7mWcBQlCjthwnpGXF5mKLzWZT9TAttKgNX5yDt74efNoE2BaISCxI0VkcR4QYsd1uEJ2XQqlhkHS0slSLhqG9ATPjaljAeTjwIff/JIV58h7JPpjB7Y8KJIpLAHQNdvt7q70ivUgEVLEgxcbmecZ+v8fxeMS6THh3eyXeqHq7MXr44BGDZHHGccAwDAiWdSv9PVjH9P+fjQIRfZuZP9P//WcBWEfqvwjgvyaiPwkBGn8WwF/7svPZ5Ost/OFwOEHCcylwRVJPpmPgva/l1SEEYJ6R1oRh5EpgAlpmqHevk6oRG5DmgwdS6+Jr6TX73cRfvfcgBchMDSrljDWtop1AXKXEUcOFRru2fpf2mjHTmBnkfeVEyLWqklLntkumoSgGk2DU3sdy4fZTQ5UudLBrsff24YQtZGmyumrVo++MQnNGK6+/CCkrpYRxGPDs2TPsdrv6fZa9sXSrI0JZUo2538t66LW44OFjlH+1Se6aEtZ5UXBRFJmyM4Pw/kKvxkHTQb1614fm48lnHzUKp8Vk/Zg7NebWG9Le1zwE2wyEuWoiOMI+nHE8HPCwlxL0ZTpgnaUOJ4aI6IBBU7omsBt8FE9KUEzAWwPhAnCSsaEfoVGgx/s+/ONE9POQsflVAP+6DuDfJKI/D+BvQfgqf/gpmQcA8DGCuSA4ApeMOOyQ1uZOe+cRN6MsZFbvTC3xmpLGWIo/KPlFqMFqI5kRCZiSyKZzITBnrOssJc4uSHMXBtY1YRgEZHfOIfiIZc0KaLVJM2udBJUMzlloxkXQ9qwyWIA0XS1OrjeBwa4IwAdCcQ4uRDgGKIv0+8Umghwr2RmgEFAYWKcZmeVcuYjCda8tWA0ADAcRMJaYUUg1IXSysqNaI+G7Bd9L3NXiKqUIFXCtYDUDIjJpombNWcK4YRhwcXFR2aB9vQqgVZ0ASnAoXnP63sF5oXKHOGDcbKUxLByGIcI5j8KEtGYgJTzc3uDFxQ6egpCJ2cGxtKBjnkCU4JC0OpI1ni9wVFDYndiE06yBEQzMSxAwW/7MIGi2CH12RpTDLANjJKriGihMRI06bt5aTkjrrHRkSTEu+wnHwxGH4xFpXZHSgjEqrVybJ1sRnXROj4APKGTitFrvg4K1iDfL2ejTTzt+pH0f9P1/HMAff/IVAAAI65LgBg/zvGD5fVvUnUW2ndEAtHlZcbnbIfiAtC4CUlkLOg0VhhjBOSOnFU47NzuizkOxrkvAxcVlFVYVLoRUL1KQeNTYdEZ99kTYbjYqRBpQCmPNCT4DGdLMdFQ3mAm13XspknLzIYgiMlg1HIWp5h2JiKmMrHaI0grDkkAkzVR6wLB3ey1+lonskHUBe2dt2dqE7V1mMwjrumpGh6vhSTnDKyBYAVDtmOUR6g42Ko5jqdsaOvXhlILDpKlRH0Qqb7vbYbvdwXdEq5QyiKSKsmhXaWjoAm4bRXPtpT1un2kgMKoqUTe/zlOOlik6mddGp3baF1TNij0db30xySEEAUMpnHoJrKGj1SWsy4R13uN4nHA4yL95mpFNUNc5xEAYh6GFXCEIOKlYC/koqdZ676IGbs/KwibOP0Kj8HfjsAkX/IicFlhOvIKC1EQ/al6ZCFlBSRu0w+EapN6B1QrYOQALFRqgZA/ewop1XatCUt9kNuUkHbC9h+G4zjU1JSMiNVdThV5YjVdpBCsxMirqQlpVCQFWvebwJTRymp04lV8HQd1CVCVpO85dVH2xjgPBxF49nDsNGfrQ7UR0hllreWRicRam4aqL3RuteF3BWq1aiWI4xTj6mLpVSTb5ddsBLT8fyimlXH4KuOTK8Ds/zsG/pwCHj73Wzw9AvC0YfuMcgukvWu9NiCcQfCt9N0/WjO5aFEBUEHE+HrAc9zgcDlKzkBIcL3COEIMYF0ehcmmcGU4zDMqDYW7l2RZiVvk9ls0k5R+zKkkQGi3XuO2lNQEpJibRo/jKwss5IcRBio9ixFHDCKABizUW5qZSDHJVlqu6wnpuW+RmLMBCIhLlZ6lJMLDIdhtLzznnTtJ+fS8LY/3ZYiLNJNj1UQgwPEjA0oKUE1xpis+WNRBjx3UX71Huftd3oFPqsu2KfOr+2r1axqTiBqypUEiO3wRxe1ozl9LIN/r9+Sxk6I2VGepaaOV8zYSI0dSej3y+yDUM0IWHjqdhIGJ/75Z6rsaEue7sdjyGH9Aj8feJloTz8PBag6Lf68QTNH6N8/7kftZ1xeEoOMHheMDhINmEsiyyYFk8Q+ub6oNXRqaHdzLXQgigMMAySmzrwOZLMf2GNrb2TNcfu4IoUCVrpNQesAFUohjkKggHRk15Ra0hJ2hfRx2YnHON8QxMTKqsbJwGY6WZ8QCaitGyLFourci8nierNqPtdjaB53muGQ3jHQAtfWfnBbqUondS0kyNXmwkK+891rxWLUa7J7tG7x1Kauk734GU57ufgae2eBro2LILJ4i9hQopoaSEsmaQkwrKtCzVoPQGbhjHNl7qthsYd+6RWNWpjX2NsY3UtK5CCwdVDoikqwuG4DB6D1bj+9hCd86h95aJWkGaXcP5vyfeGHqKd8Pt+3sp8GiVq0JHN+q4kY64FK1WnISSfHzA/vCA4+GIeVmQ1gWUhboerOox+C4l7JSgFWsRVHaD3US9LiKcjC8YoBhP9EZ/7JrB1J2zS9f1O0yf+mKW4LE+KJU2t/jVJtk4jtIoVQdrXRPGjVRhxnHEpPQ6cxOjqjXZArGqR0GSVVA1207adv5zmTgAtTGLuHJt4pHGz2AB7MSFF+Coz1GbEXFn4QGXAjibxOjy7qeKy6cHnyz6wudLqHtnZ4xlzFYBb9XAWijh/BmKDsBpmGAkLrv/PqQBUAG1HtQ0z8IwDFmMAoxa1kJnCpwTpD1NzSvgwlZpXT2pnE7DqmLFVN1z6u/7HIs5ySSgZRvEaAewHxq+4By8M+Krbk45Y52lcvFwOOB4POJwuMOyzLWq1cEhBKpep5RDmzMngKW9XruU+wGEvtqVT+T+SykwzS3JHiXMbj6pwfiy46MwCgDDOYBzQcmoi5xZGG/zNKGUjM12K4VDOSEiYOMj1mWRzIUsQQxhAKcVaU3KHxfLmvOdoOGZteqRqtCKC1YHIBNpu902AVIAJpC9rIumE5sHMk3TKZikRsaFAFaQUHLHLGm7JQEbMRZ5nkFBsicFwv9PKYNZG+g6h+zl9bUkrCgY2YEykDPXPhZAcx970CyEIClRi3vVS7CMgYGUPfree0GS34bsfoa/hMbkNGNaShHpdPOKgM4bOz3MQNlCqOHKsiDPEk/De7BbBfBbE1xK0jNzjNiOAYGAwgk5L2AkJFcAJ70bvHeAC1hmrkZXnstpluH8sOfpvUeGl94bzgE+SNjIzRh6H4RYRcIhEUm/gqIA3zzPmI4HzIcHCRMmYSPmdYUnqlyRMAbxcqvhFBEV0noYwxBiHKo3SCRzqfYUdaJvOo4DLFtCCkpb2DAMx/daJX7R8VEYBYv5MhvrrVS2XAXn0gpoaizGgHWSeohBCTBEFvc197jt4PbDSGmFjwrEUQNoLBTwrqXnmGUhW86emSswaO76drutOg+WjjLkOKgsHCs92XbJFy+apoJpUM7LjDEOylYDrJRavVO5H84q0ukBNFe291Z6t97y5URLC6P03+BDBW0f2zl7ujOVVsZ+jg3UStGOSly9vs64WxjgnDthmJrYrhjhBIKvTXQIWcIVAJ4k3iZY+LEoh6JotgH1uThSFxs9mxDVyD1mrIBOh5IawzCEoCzXBqA63yp4ZZ4llCy1CdNRMIPpeESaxUOQehtlIOrn+7Cpp8wLqcnVDAO5AB+ihJpEMj5ODZRuPj4GhBhVEaxIdmZdwSyAewjhR1sQ9XfjIJK0HCz2NYKL7k6pA7+MFWc9CQBxSc3dtwcluwPVRcdsjV8Z2yEiFyDlpbq1ABBCrGSaKiLrhT1XQxKVTLfr3m63J3iB5OzFgIzD5lHXFJBsQ4gb6e2g2pI2aS1Fam60c7665Q1opLob9PfQI+dZ04mn3IUekCxd3Qa61zugMgTktCJr8xR2JPx8NTjwrube+2yO3WcliqmR7r0Le56GM+TU7kXG+DSlyaXgeDyC9JyiPyhG1Lw0Zu5eo4pPtfGl94xCD9LKoownRkHmWS9xDwlhS64cg2U+YtJMwuF4xDJNyGmpYxtjxOBFKPjcuNbvtUWuRkKMq6gr2f2RH6qHYjicNXthlnqJaf+AeZ6rpyAA9dPX40dhFJiFcLSkWWJN5bpb8GsZA4tD4xDFU6BWs7As83tkmRjl9oZhwDiOgpBDQKu7+z2sg09KSfsHSB1/v8M59QhswtsObDLum80G3ntst9vqFqdSsKYVQ4w4cMsMWHrOGt8UpW2bESmK4ov3Yzt2rlJqlQRj5ysZZW3FWX0q0QwCgJP+kAAqQGf305+zfyYVLOUCkK+hBGmbOlKgFCu1dKdef1BSmRnQPr1nRt0MRQ9wiqFYQCRueu+RCEltxqhydSmZh8Qn516XCda5uoV1UB3QD9+zGSx2oVYqeiUNmVEopQhzc13EIKwLjscJy7zHdNxjnles66L1MuiaBEkXs94omGFo3p2rnkFQnInIYxhll5dy91Pug43rPM+4v7/H/d0d9rdXtcWAaVPEH7fwAWi1DtM0YRij9Pczd1RjY6DJhxVmBDUKVlxSH1rOCNrF2uJ+yWJ4xMHek0yMV3pGDgPyKpkOc8VjjCdy41YB2U9wEcVca/FUvyuXroqxX4TiCBBKzhV4sut2IFARZWtROjacwmMYIjxrURgRfHA1O2EGst+hhZvQpUedtWk/LSiz3dk+0x8GGIIAF8StNQ6CudAmz26hhJyr1TpYurMXrOnTtFLlKlkEM8DD0Mvgy86dZ8lMRH+a0TCkv5SCtCq7VVW2ya5VQWWjhdvCssO+K8QICuPJTl608bHtvMsyY52OOE5HTNOsnsIB6zrX573dbmQzi1LaTUQYXOcBGI7gemUxB8RBSp4r18EjjpvKYenl4kspWDW7cXt7i1evXuH67Vsc764xTccKjsYw4OLi8slr8aMwCrajDcOAstuBucmDOedU9bil5QwETKBaehuCxzR1mn6QyWc7+jAMIEfYDCNAhBgHpLzUAitAgMTNMNYHKxLvdJKahLL5bNIcDod6TRcXF6IgpfyDlLriH+M8wGrqNYfMpZKZKBBUU1SNnfSFsFoKAe9kEkFd4UoU6sIGQLwI7027QXdqS3lVebemfGRGpV1jU4piktN4RcG999J/o2paNH4JQasxV3qP4mwMUMuZmxGnjkhlRkok2wZpCxgbO3JdV5QhKn241Uo0Xot5AagZHsOYJFYnOAoNcwFOnneMEdBNxs6btKYjrSuO04RpOqKsM47Ho7jpJQMlwdsYKaYCOvUEovO15L/PypxkO4YN4jDCq1dBvoUPAFDyKuSnadKMxgHvbm/x9u1bvHr9Gve3tyjTPZzz2O222O0u8OzyGb7xjW88eT1+FEZBZKwdGKL2a7FlneylIJBHdBEeHtGZGyYLTKr28skunUoGggN7YC2rCHoy4fDwgDhIWue434NIulwv81ypoLZjrusqwKGChYJzZngv3Y9JF9duHPHw8FAXkVe0PwSPxevnCVIgo0BSLpL/d94jOsLR4npd7IUdmGRS+CgAKhegBEKhAk8FRnP2JN4FaawpGi6STSlFahZETEHy4aZA1FdrtgVSIOUqwhwsrGKrhUGFEcnDwyFnwIMRmBAgNR2VDckMlFYAZd7AWrKoQKmsXO2szYSgDD6QhH0heiASXJEeDWsBUi5yDeyB4AGKcm9Zsg9wDhQJjkfkZcGcMgpIgV4H78eq+Oy63VkqCUUSz9OAAqvULViWhHU5YJmk74R4BTNyXlQQVUlc3iP4oe7o4pHp72ocEJVe3oUPioBq30+PIWwRhqGqaoEICRklCXg47Q94uL/H3f09Hh7ucXN1heurN7i7ucV0nEDMCN7h8vISn376TXzzJz7Fi+dfx7NnL5+8HD8Ko+Cc1N0/PMxCDQ0B8zxjs9koEMbYqLXcjGPNEAh6nk6tsaboJA6milEsy4o1JYQYRco9BHzrW9/C/f29AHa6q/c7iCk1F807g0XfcV48Ul5RmHFxcQEUxna7bdaeHCxbbB6PAZ4pJ/lJCc+ePatjMKjH452U1ng1JhXl1mtc9ZwWIu33e4Q5tB3Hd6lR7+Gh/4aIOR+RS0YwIJbUOyutJyd3XpAPHiVJaDCErk6isHImHNKyiuqPfuf+cJCS981YKRalFIRhwLPnz8HmPel9nTDt1LUfxgFxiChEmJYVpSQMEQgxIjqJj1Nu5dvrsqAUwI9bDIAyXRtJilgK3WT3FcXpivY7rypesjjfPdwjaUggocKKdd4jLZMUfRUGo+lcboehZgJCd17pHuYq7dt7DxcjnN67vSfoODurnYBDyhnLdMT+eMTD/R1ev/oM9w8PeHd7i/31O1Fe2u8Fg3HAZrvBy+cv8Nu+85P42te+hucvX+DZs2e4vLwUZe0CzGuj7X/Z8VEYBSt+YojraeCMTdCLix0oSz2B7T7J6gPQej+apxBCQEZrzrLZbFR6LVeh1k8++QTTJJr4lrmou5rG5hbLFsp2mRUo6oVKSQVi+/PY0bvh9hljQD579qzWcVhWQLCGAlcabyJpy7MQZOdBbgrQPQAKiExa9k1LImkcDi81ISllDDB69SkGYeNtxWAVjFMDYzn1omPUgFO5f+vdycwI1O7felkMw4C1zPV1+97+6K9nWqWZSQiiF1CYkGfWXTyLF6ReSBPW7anwqMpW3ntwDIpHRNWSUMIYF6SSkNaEu/s7pKPWIqyykXDS4isyJq2kKb2mJoNSkmvWQFxYFNjiF+Dcx1GxIvFQrHDK2v2tqwCpDw8CGN7e3uLm6hrf//Vfw3GacNjvkedFlJq8w8sXL7B9/gwvv/41fO3FSzy/fIbLiwuMuy02o4TJh+MRNw/3uL599+Tl+FEYBUZTKbYdB2ipxpwLKOcq39XAxeaGrat0CjLUVWS5cn2vvf/i4kJbpj80cEmNSosh00kRjzEZmzowaZy5ChrfgX1EVCnPtsh6mXj7ew881j6K0Jg8SyxsQh6VxclcMQIAJwvA0plG/OoBLYvTxc84Pc6Ngi1wK4ayOgvL7wvI2AynvdfYpD0JihypUrVoB2jAAmnMcqp1abunXc+yiNajdxHbjQi2LCsjTVzly1kVs6W4rFW5EjU9hpwzvGYcJC3owQrisnIe0roiJ1Gg3t/dYT7cI61rBYqdgtrkXIcJuGYIrFahw8DgHIqOvyhHRQQ/VPDYUsuGNU2TZA/e3V3j1avPcfP2Ctdvr/Du+hrvbm4EDwkBw3aDZ88u8cknn+DTb3yKy5cvsbu8xMVmizFGBB+A4JBKxru7O1xfXeP1m9d4+/btk9fjR2EUAGgBTUvdWQWiLewBzWA0HcSWg7fYzkIA7zzW3ADLnBOClubaRDSv5Dy/3qPx5BzGccBcirp6oRbNOqcakWp4gLbIgOYl2PkNnScilYIzLoSr4iuGGPd9TsMtKgAAIABJREFUEHKWXczOaRJpVvpcU281nj1VMTJ8wTpk20G6y/U1E71xEFcXcJoR6L0Z4z9UboDepxlTM0pG0YV25IZmGnT11vGwOLtyHCzjshkqF6TMRcMB8wrlnKdGoYnZMIsQjdWtOO25kTIwLXOrXZmPWOcDjtOE48M9SprFcLF0y/KdLoItZLCvYWEITQSmamg6h9KFCjFGxDACmg8SXgGkbHq/x/XNDV69eoW316/w+vPPcPPmCvv7B5RlwbPLHS4uLvDs+XNcfPICX//61/H8xXM8f/Yc24tLASZB4CyCP/vjHrfv3uH66gqfffYZXv3mZ3i4v3/yWvxh+z78twD+Pn3LSwC3zPzzRPQzAP42gF/Sv/0VZv5DX/4dgA9KU9bWb722oSNCWVeQD1hLwuAZc87YBEkZCvcN0vZMP/NwPIprFwdM8wzyHp988lIwBJ3EfQrNKLe2q9pCK3mVuecANwYkTgBlDANhWZPuzg017yshY4y1n4Uh9kI7HWpGo2IQzCg5IUQNm0C6kAzbl65N3kmhUM7NU7CdWr6n7ZCAUKyjltrO+djSeMyVCSjZHodSEnLuWID2cKBlD2o0EhdRuybRq0g5IYOlz6UqAAk114g4USTcmVCKeFrOC1/DDIl3VA1KzdmHQXggwaFwwpoWCSe8pqwdgQy80/IHMRgJlBcMWIHskKOkB+EI07ogTdJRKa1JVJyWPZZljzSvQFpQsupkOOmdKVWKHakoBHBQglMUARh4adBLNfNAGFysnwkhoMDVCsZlWbC/f8D9u1vc3dzi9evX+Pw3P8Nnn/869g93KCljjAOeP9/h029+Ay9ePMfLly9x8fLrePnJJ9hsNthsRhAFlJRxWBZVbHrAu/t3ePv2La7fyM9hOuK8Pd0XHT9U3wdm/hfagqY/AaAPWH6ZmX/+yVcA1EInGzBLP9lgghnLPGPwHuNGlGpzUUmyeRayTAi1aSlBCDsUQt15Q5AFejweJdetgKTlnket8ut1BM0VNjnygQfM89IELUpRncTmWbSWdKf5/j7dZ0cl5sQoRS6lVHKNhFSq4xeEe9+DgSY91ns1AE7a05VSTnZ0fXbVI3BBqg3NOzKykl0bmEU1qpxWUZZSVNHaDExHMGOuKdsTPgB3moYkO3wfLpgQjGV+GID3ojlo+IU0Bcpgp/J1Cko7p6pL1Cjfgj3lShTL6wJCQVoWzPtDDenkfYvUUSTtf+FjHROn8Xvtw2Dh0Dh23oPXdnjCS3AWWmhGw0LQVBYcpxmHwx63N7e4vnqL6zevcf32Cldv3uL+3R0O0x2GIeLF8+d4dnGJy2cX+Pa3v40XL55jd3GJYXcp+p0MHPYHzPOCaRKD8O7uDvf3d3h3d4Prt9c43D+Ac8Hl5WUNp55y/Jb6PpA80X8ewD/x5G/88PdUVp5pIwJCIDI1Zas3mKZGC7YFmNGhvGat0dB/AioQZviD7dTnKsf9T+/ykyLU3om6UkpZxGL5tBtRf0/V2znDLuy7AN3xvUdJglusnESKPDR33zymFj614qvmJciOZYaid+3t7+YWFy6IfhBlar0Oi4kraEdCc5Zr7s5XtDqUASj6Hl3EOI4VCHauYRqijSAG28rczYW28bEmVH3YUxR/MRAXQKUpMxeVSPcaEgqGkVKSCsWUhNWomZJ5mjAfDyJ/ptkFCTkhHlJJ8EyIPipsowCi93AuIAwRsRM4cbXmRsV3iACCGg0BHlEar2aaJuz3B9zc3ODm5gZXb6/w+vXnuH7zRlq9LSuierPPnj3Ds90Fnl1cYnd5ga9/85t10/LeY9E+EIfDAff397i7u8fxeMDD/QP2hwMOxz1KyhjigPFCMjk/UqPwJcc/CuAVM3+3e+23E9H/DeAOwL/HzP/7l51Edt7Wi7FHwC31ZPHaMAzaM6BUMC/GiGWeK0W4KLkJ3PQCghoamwwvX77EZrPB4XCobr+BcsbCa4QlW8DSz4HhsCwJy5IxDEUk0xyfLNKeBAQInmChhMWytrCjStuL9yNMPB9djU9bKOWxrM1ACh6QqrdTwIhDaPwOvXbzvuRaBNg08DBnUQOyXdoMSgVeuVOtsvjdxlfZjebNmeBK//mszyHZ9VATPiml6SuIMRPGpVR3lvqsbPwMNzFcQoxsM4BgASHBrC0DJTsRGEjLivu7O+R1AbQhEFh0tBiAgweI4X0EaaWitW6jEOBVyl/oxwFeS6dJPTvjoJhRYwDrMteFe3Nzg7ubW1y9fYur6yvcXF/j3e0t5nnCEAe8fP4Ml7sL7F5c4PnzZwocDgiD6FU6Jw13p/uHWo59d3eHd+/eYX+Q3pFplTkUXcBwIZ+PXTjz1OO3ahT+JQB/rvv/zwD8Nma+IqLfDeC/J6Lfycx35x+krhnMs2cXJ6i1udpEhFV39m0IWLR4x9J4/S6fc0ZWV/k4Tdg/PIB1ge4udliXFdHLrr/b7apXYg9RUn6hgoOWP8+ppROJRBG4ZJGVj0FSTLbj9ThIr15k92PqTNvtFkDbmfudUTCKAg9RVpJJd1pwJLTsEVIotbQ0qne1SOykCKpD4sVYyOIvzDUta9c/juOpx3MS8ai2IVpFoRnLEFw12MY47b2UgqY8ZUbrfJ7GEMVjKkV2w9QKqEh34uZdGf3ZUsA90NvVm6gRS+uK6XAEcYFHQQxtnBiSQnQMDMMIUqS/ahiEAArCVDRVJe8bQE2KJTj1xJZlwTzNuH93g6urK7x58waff/45bt++xd3tDR72exUlBi52G0kl7i7w8uULjBdb7HY7bIZRCqjGASlL+795mjA97HF//4D7uzvcPzxgWaaq3zGEoJW3A4JzGEJAdKIV+SENjceOH9ooEFEA8M8B+N32Gku7uFl//z+J6JcB/A5IF6mTg/tmMD/xDe5jXgB1h5imSVBvlgowUeVp7r2FFAJaqQ6AIzwc9vAh4jvf+Y7sSinDB49PP/1UQpKUpL7d+5NdcponIRLpzjaMA7CgegEri/Bmjef7eLq0qkMiwlGv3WLcFqe2fg+krrBRlq3WQcZCKhOdlx3WYtucRZ7eKMeGQZhxOvdw7D1iFIpy8YVZaKFIj4OYITGcpz4zoLaw852EmgCjfccp1aVULMJ7Dy4mHqOckjMhUa8LzvQIlnUFSPpQ1N4RRXtOQPgConTlsWqhE0BqKFsXKM5F6t64qOiOMEAdhKshVbEOcB6BHDbbTSUY1fENg3gLzjXik28ELCJCgQj5HHUHv3t3i6u3r/HZZ5/h9atXePPmDab7e5C2CdiOI7a7LXa7DcYh4nJ3gc0gvS0sVLTx3B+kB8R+v8fycMBxf8TheKhZuDBGwTGCdFL3TkVZnJN+KQqUP/X4rXgK/ySAX2Tm79sLRPQpgGtmzkT090L6PvzKl51Inhkh+AFpfcAQPVKSECGGC7H0WjU2eJEFW3JBIIeShYo8+ChovfcYtiO2my1evvwE3/ja14X9VYSAk0vBtMwaow6KqgvNWqobxYKDhCIM5+FChI+DZD9yRiHCyowEwBXG2FVjGjFpc7HDmhK22lQVaC61xexmTNKaKm4CT3CjyHYTEVDEOOZVKLuk7jN5D/YOC2csnMHewQ+N9AVoRoW1KS8RXJashifZFddckEAgnUiegtDNWcKkPkWcc0YxinIBovc1hZpLls+ScUNyrVKUlKRDXrPen0N20lHas4P3WlJMYmDXLFzQ4p1Qh4MDESPnFZyEq7I6xhAihkGk+RMD5AIcSav7zNJ81gEgLiASOb0Y5LUYXLexCMkIzongziYiarbCO800gMBOBFcoBsAHZBdhXaLSsuKwPODh4Q53t+/w9s1bvP78M7x59QNcvXmDaX/EcjgieGlYZM12NpsNwhhqWJJR4FNGcQkLQ4DVQ8E0zzjspbP0fJxqSDbECB9U1k8B0SEOtUGRGS/bNJ56/FB9H5j5T0O6S/+5s7f/YwD+AyJaIR7jH2Lm66dciO04IUgrtYtxh3mexUswIg5kp1rmpTYNtVgyhABGwbAZEIcB3/zmT+Ab3/gGmBl3d3fw3U7uSEhMD3d7LOuCECQPnpPm/5UMdR6bm8tqMm+GdZhnYGFIzlm7Sp1mMQCchDsWv/fqSf1YWPVcjw3YdwmbDvVzZmD6cKFlcjKWeanS8ua1G2OyGJbBjHVNGra0vD+MYqDf1aPugk00zghrXO/Xdi12fzEOUjNCRgQKcAwEJTZN84SVi/SPhDsphOrv3XmHoMrPBlgWDW0qBmJsEhYPyqTnHViKojo+R9E5Ng4Dxs2I4MfOE3VSC9FnU+TEWNIitQh397h+9xrXN1e4ev0Gb169xpvXr7C/u0VeVwTnJSQYR4zjWDtnbTYbxCHUDEofDs/zDIZQ23u15/5ZGxWduudhc0Ay2mYwvoKbgB++7wOY+V955LW/AOAvfKUraJ+tdQ6ibl5q7YHFh0ayCSEga4cgItl1gnbrhao2LavQba3TFJiRV1EiJsd4eHeHZW3air3bXVN2ikm0FFffBFeUkZz3cIWrAbF/13XFEIdaWwAAx+MRy7Jgt9uduP4WnwNdipELCkSvoJQCHwNi6mjUhDopehym9qLozlfYOmJxXZDOOVAVdC0KzBVte9f6Lzptglob+oKEKGNjhpaKNWDQPAsziGKMpBy6BzKdB8qqykUlY55mUBTpcmimono96r4nalkhHxQMhqaIFfDoU7BAU1vw3otRCE47KmkmyDmQDxiGiHHcgBBOnguDWm+FUjAvE6Z5xXG/x93tO1y9eYNXbz7DmzevcHtzg7vbd0jzDO8Jm3HEZhjFIAwjhiHW/prjOErzXHeqsWmL355nP+/ZtVDTKNQmGvtYCNizWp96fBSMRkPBARVGYeG19xmBdV1ROsUjKzYSFzXUHQwJWNOK3W7X1ear2OWaQF5y/MTAZrPBpCkv2blPawl68LDF5aXiB0McVCWqDWNvXJZ1weobw28YBrx48aLuBMbF6HkAdg6hEipW4F3N//e7s/SzlO81g2TnqotPswbMrSVe/Q47uFWG9ovWsgV9j09mrr0G7PtTSkDJJwrNlhkCULMduTO2lZHKCWlZkJPgENuwUS2Mph5lE35WXCHnrKBfUI/gVGOggqwdT8LYlQ6A8wB5KwNXYROv/TrjCPAp5Zo1ezHPM+Z5wn6/x8PDPW6vb3D99i1effY5rq/f4nDcYzlOIAZ2w4DNRlu7DZIWHOMgoUIIjVvjmthL723afKiEKa9sSbT3SqsCYatWz8G3NPb5eZ96fBRGwXYbSUNK6a2JpZqIqPcewTlwLoIiU9tVmVlAqtIWsIFoBkRKFZuctdF6uxbllqajU0Ube80egCyuTrlYB9u0G/rPRN/CAnsom82mZgv6xduj6tDxcF76KNr/yyLquAfc5fk7ENPOWwHIem5CIGvUm2onIZA2z6HT5jl23XVyaceqwqdl6oIhNMq5TdD+vnLOleDTP3nTW8xZ1LdCjAjaM7LP4ljGw6TfTBKdnKaEmKuwKhcJR6RiXOnZJAxTTwAFyyAYDTsKUcw5eG1kbPe/rivmecH93QPu7u6kPuHdLW6uX+P2+gY3V1e4v7uXzmNE2IaA7ShsQ++BYTMiDAP8GBGpNYuJ0cN5Auh96nn1BvUaeqnBQL4+07qBeXfiedbR/QreQX98FEbBbuN4PMovLMReK5CKmg6ajscurpSJAx1AihFFZc2CDyfiKXVREGHVwc4pIedyolRsgxhjxP5wwOCaMOm6rpjU/Y8K9nFp8bUBh/09hc5o2aI1XoQtYHuQpltoizoMQpSxcODck5DcM96bBJU1abu6Bd32OeriS+6uLQRstlSzAxY+kKZ5CxEYQsk2NadaVcqncu7NG2iUaskaKHUYuvvmFcxS/BV8hPMqcgINYUqrWu2f5TAMVQZP7qev8PTIyXo/oqau7bPeS+9OMz5GwxbtAwlbChcs81xVmO9upbDo+uYaV1fXuL+7xf3dFQ77PdKSEL3HbhwRo5dQYRgxDgICxnEAgoDAQZmOUtwkUmvo2LD2DPsfACeblXdnRsS52kKuOk36b/N0vppx+CiMgljxESmtmJcJXhcFsaD7zIxAAZ4C5ryAtXFqZuHhe+dBwUMqXCUdFUPbpXPO2Gx3YF24jiSFxJwlV8Pijq7rinlJCMMGII+Clt+1xbqJUTCEAgQGRiceRM85SClJezVeETTvfw6WWQrUFnxfjGW9FDyLsEgpBS4OYNfIXYzTpiq9h9B7Ipx1DIix6gJHcIB3QAEiOSycOqwhQhJ2Wb2xVlwlNQtSeFONEZHom5DUIZSVwElJSTnXIioLRbwjBGKsWWoPnAugwSNQwy5KsY7eUsFIzNgMjVa82W6wvdiJFLx2tiOCNOUNAAVGoSZsQ15qFGLeyrh6jxiabDqTNO11DjgcD3h42OPh/gF3t7e4u32Hd1c3uHr9Gje3V7i/f4d5nkD6HHZqoIZRdv9xGBFiwDCMCGFsnk3wnRGTbIk9+3NG7WNGweYHubMQ14kYC3l/alC6kIrUa3rq8VEYBSkdnU7atZnMt+RiW6OUlgtP6i0UBBJiU2U7cqMvVzlxErBqXpbqYRALpm4usA8RYxdL1gXexdtGOqmgUM5YpqUBQV0YUt18oN6b5dyNvus001HDg25X7BF9O5yNTbfwe/HT6rZrzJ759D1eWYBSvnwqL55SQqFcc/wykV0VAXFOBGC4dGrR5VSH0jIZpnOQAe2L0LgIlrK01wAgOJVSJ9FbTOsqhKeSMQbtnuSDkMZUO7MojwPcOobZ7Bf88BSV5yDKT2Hw1d12ep5lXZHWFZ9//grXN9e4ubrC7c0t3t3c4O7qFg/v3mFZJoAYm82AIezgnccwDlrgJvhECEKoGoYRPrQekAaY98/SNoneANjrNv96L8E85F6R27ybx8DyehiN44nHR2EUzIpdXFxgf3hAyllqw3UC2KS22oUK0hBVIVFTNjJswnZjW2DeeTh0tFpAgDwDwwrDe/l9v9/X+oiST104HxzyIvJuBVxVlJZlqXqRPVmoL4LqvQHg1N1v19E6LT+GGjsiBC0rr+g4txizLjINi1YtufbBo6w2OSUHzsCJUWjX00BL4pZyFC/LodApoalNOtWjUOp1f+UFIlnmvJR+mytv1z2EAcs0A968OMnsUAegJdsZIYpN67IibtQIqMyaDdf5zguIclNwrc7C0P1lWfBwf4/7hwf88ve+h+sb1TK4ucHxcMBynOGZMG4jtltJK5pORIyx1iWY8+Sc8gU6bMWupXfpe6P/mItv7+8zXu7MUJj0Xn/P57/TVzIJH4lRqCCZpaJO2H6sC7a550ZptkayzrlqEPoY2wyKuKOpgpfmQZRuIdmODqBSdWXnlSGSdJ0AXXM+IlqfMEfY7Xb1PmIHDNr1DMNQz9/H2jYZ+mvo3X87Zz9p+n97INB+LKSQisW2i3vvwVqj0LuYdXIxd9eFWlZdsYnuWdnBzJW1yGg06qC589Jlj0D296AgbQCbqCxJynhNSdz4oADgMCCtq3gnujGUlLGsK6bpWEFSADWlmtJ6YmgNmJWWawxPoo8pAOKM+4cHHB4ecHcthUrf/aVfwvXNWxHkXVZ457EJAZtxxHa3wWYj7QK8eS/Bmr/GE7DY+9CAT268jz678EXroTf0PVBth82jAskGFZ1f/Tx+7HNPOT4Oo+CsqEY488NmU0EsQDgL7feWy3U64FlDDaArx6WG5OechdqLhtg6J01b7MHVnVfLcc2Y5JzronZEYO/h1EuJwwCNJU4WVp/KtF3E6h5sEjWg7DRd1GdCjB9hntA5X6I3Cv1OX3dJBfZk4Nq5DexalVdg3yFG1EIfc19Pr40UiDNJtj5bYqEYo1HQeyMmsXOEjxE+exQtiFrTiqxqTpGsCMkjxIh1nrFmYSiKKGzr50mq15gtHKSmBGU9PG1nLZqtySVjXhatLnyHt1dv8e7tNa5evcHV9TV+8IMfoPCCIQRsNhuMMWIzjNhutxiGgDgEFe0dTrws5049gsoJOTOi5+79uZfwmLE4B8zPj/Nww9LXP+zxURgFKCxSsohw5hgxhFMUXBZC24GFnSYEF5O/grlWEB56yaWVnAYvPAci+CBdkhMYIUbpQKULzXo/sNO0FtpgCyW51LZhKSfwjNoI1UIY6A4VvK8is0bmqamlELDdbqs0mxmSPhTpvQRbtAZQ2rgAZ1mH7nqdE50Be812bzFksrv2eIa0PyM4Ta3WSV2RbFJ0HlJ8xgXEVnehRgr2tt7T0+5WQFU2NlyjFbhZFqTrQp0zlmWFQ2tgYx6M906zO03OTfaClssvAKC9ElLJKPOC+Tjh7v4dXr95g6s3b/D6zRvcXd/g/voW9/f3iN4hjjsMWnQ2hojNZotxHBCiR/CiiE1OWrnZRuNcY5hCPa3CBcinoaEBruiNAtpHC7calv55fpFn0ZiquumVAiYSY/lDHB+JUbASXdnFgvPgLOCPCPU4W/PwXgbLE8ExYwxRy0ZXDLstWBulgHHaZ4AIiUVmfMkZG30YhbV4h9QTLQklLRA9dYJzo9CtbbeeZwTl/XIq8EPXv8BcRDTGo8S5bXcwPMPiWQDVIPQAJXCayqze0dl7ztvl2VHPoaEXE5Bg3bvR5NU7YHVdBQUfKKjBKHAUEFh4AS4EGUNHSM4hO8A5RqIs8vipYHABJTg48qrUlCF6Ew7RB0X9B5BLQFmkMK0U+EGVjIwWbhmOIqrR5AgUHKhITEPM4JxRSgJzBkiKpUIh+OIAiEJzIsJUEg4P93i4vsbDragcvVVVonc3t6qzsCAAuNxu4TfSoWmMAwYtyApR28F7f5LGruNNvYgMxCth6f9pz7t7OG3muzNegv44aSVdqeUfCgVYZe8BhgvaSBck0vbyYdlyv0IY8ZEYhSYAYuq7Qd36aZqwu7joaLenlGAiwv3DPVLKiJuxWmLnXBUDAVrNgQFwZsl7bsBJ6qdzR+31NaVKogGasjPQdAlstx/dpoZFa25qTCbgytwo2JvNphqWyg84W+jmQZgROMcYejp4LVcurY+j4Suz1nfYxDSD07yxFor0Gpk5Z/giLeMqwOk8fNAKS4rY7XY1i+RDALrqUAPl7F7t2vvwyLygHiTsswep22E/dPQdvFisPsCMq7dX+P6v/AruVQvx7vYWD3f3SMuKIfjqUe4uLuCiaEMa+7DHDnqQrx///ncJXx830vXvj/zev1YBSa3peOxv5/9vhqXyR868x6ceH41REMXkpk1vgzgMgxJeOr63E+ktu3lHDuNGREyia0w6W1xRrXwf8w/DgOM8nTxIO/c5mm/GwXCLnn59AmoZo89L+a53SgGmtgD6id9UihoOYSlVu6aaNuvSV7ZY7bDr67Ma/b89uNmj39R9vgJXZ2CtSa5JqlGAxXOw1HuP3WaH7XYrXpuW9IoeI05ovb1XYnqVEhoFIZN1oZHd/1eZ0CZ7f0IOA/DZD34T3/s7fwcPt7e4v79DTiuICdvNgHGItRbh8vICNIRqBE5oxo9khc6zCTZWAMAdHnPu/veg5LnBOL/fcy/h/O/9NdRn1dG9H/vMFx0fjVGQUlul4gZfuQGy0ziMupMY7VnKaZuoCZHDkhZ4WNWbeAPGNuyBQ6CpH/USaTbRTUB07vQfq6fCp+pKlsk4f7g9+GTX3ddRWF1GDwyeeytA28n7id5Au/cX/fl1nANbNR2rGIN9V3+O/lpMaMR5rWdQA5fV2NkitLEVPUthi55nXPr7NeMxjuOJMTPgE0CljfceVO8Gi/7kWbyu7zlZhIXxmz/4Pn7j138dWBNAosy9GQbsxg188BhHCRU2m1GqckM4ue7eINi19sdjGYX+f78wg9CHFjjFY6jzjvrv7/Gmc7Cyf+79vH7q8dEYBe9DXcQhRCzzBGv77kO7TJt4lgISZLlIIdXaI+yutmrv3WjrTrQuawXQWsjQ0oprknjfHpiBngShSDPrgkUb/Gqp60MS+TfyraeEnfM8P93rQNpu3f+/vWZGomdDnv97jmz3BqPPXrju3vvw4SSGtXM7ZYJSew45Z0RvLdM7Qg1Ox9U8BHt2Ngb2HgCwfo12jb031cvjfeWDpaZinmZMhyMuNxsJFWLAEAbsNhv46DGMosEYxwioqlLbcNq9tdM+zis4+eru9/48j733sZDAxrIHbu3o58+5UTjPRnzV46MwCqRoci7QGnmdlGgaf/3Nn8d303xEGIKITmiTjzgMcNZt2lxSiF5AyhnPX2yxZKtXEFDGhEWMfBPV8FShVPQD3jwGW6TOtaY0tphTWuFJzmNcit4o1NCkIyPZDtvHit63BrW9x2Dv6yeG/b3KtJ3tOnVHzqe6lP1RP9+9vq4r2BktW7EEJSPl3PAZq00wMLX3bpzTwiTvUIpoHIqBaQbc7skIa8ytLR+zVLieO8OaG9EemZbPUtCOCF/75BNsNiO244jNTrqMj8OA7biRtLSK+MQYQUEEVGzse8MKnHpffRjQH9z91/5+Ero9gg2cj7/dV9/J6/waPsSI/BAw+ZTjS80IEf00Ef2vRPS3iOhvEtG/pa9/jYj+ZyL6rv77ib5ORPSfENH3iOj/IaLf9ZQLKSWBISo5hXNV/3UMxDOlZClQWgCUmpVI6woUkSTnrlX5drttCL0TSitZ804mURxyQar/MsO7AMAh+AEnxUOQh8OFtIYdArJZU1HbbbN0knYkCkdesQwzZr3XIudsO7T9va+LsJ8+PWuf60HXfjHZGPXnr8+TBfGGI3AnNGIL8pw7ka3prhYoAc3oxCBGmNDSrOM4IoRYm8DY/eacRD2JrZLSwwcC9HkzN0PY4xUnoU8R8pJnSF8M9VyYlJlJDuwdCjGSprCZGcu64njY49lui+cXG2y3Aza7AdvLDeIuIG43COMOLm7gonR87kMGM5rnBr0Pa87/1rvu517HuYE4D01OPIVHMIZzL6F/7gBOrvuHCSGe8s7q5rClAAAc50lEQVQE4I8w888B+L0A/jAR/RyAfwfAX2bmnwXwl/X/AeCfgsiw/SxEmPVPfdkX9INqHPY+HjUqam+1zSUV0VFUF7MH7uzctgvbrmNxbP+QbNAs/gegIYqrr/WcA/v3vDaDwRUwtHP3nkQpTcDVXrPrNECuv/4+5Dh3Wfty2fMJdO5F9H9zzhR53r+G/r6YRc69lALwKTYANH1JhhShte9oz7MHRRsu0iawhRPO+ZPJ3HtF5j2Z6vTpBD+Nt+06+p205IxhHHD57BKXl5fY7S6w211iu71AjCOiZhmsLqWfC/0CPH8G/Tw8PwjvL+5+vp/v7l9kBJ56PGZMHsuSfNnxpUaBmT9j5v9Lf7+HdID6SQC/AODP6Nv+DIB/Rn//BQB/luX4KwBeEtG3v+xmbMdb5uZiA+3B9NmD005CIjcGNItrD88APXNhbYGaYQiqrdgDlv3Dkv8/BSeZre+lGIjCXIuuzDD1YqM2Qfu4vn9QNgGsCY1zrsp19Tv4eUhhxqPf4c9ByH5xDMNwAur1wqnnk7QfP7te7/yJmKhdi4RdpylUud5UwUT7ewgmriKcAjOOwgMQJST7fL/DAqrKbB6PTXCyEmldSC0ABzSVxyyBxBAHPLt8hstnl7i4fIZxs8Uwjghx1DLq8J4xeOzn3Ch8cBf+gnX9ZUbh/L1PPb7Iw/iRGoWzL/0ZAP8QgL8K4CeY+TP90+cAfkJ//0kAv9F97Pv62heduE3E4Gv+nzSeB6DKve5k57CFBOBkMZRS6kPpXefeoOQsst+2+A7WQr3TVOxd+9qMREklIFRuv8mM9xLxdj3mnTBzbX5rXbXt+sz1bpmV0/SrXcu592C/90anDyP6ieAVUbcahKxsQSNQSVowVm+mgpsww1JqhFy/34bikcl47roSAYUzUl7AkLJoM6LOiarTOU8DQMtGdAbL+mwac/ExD8n4OnUT2IzYXeyw3WwRxw38MIhgbYzChuX2vPtQ4fw4j+PPF/dj7z//3GOL9jFP77Fr6D9/nhV5zEs494SfcjwZaCSiS4j+4r/NzHdnVoiJ6CtBw9T1fXj+/BLee8xTrgrBwPsItdUh9Ls+ILtmjAMY4lIXcO3XYEfOGcu6VJqtLNgFz583S2/hR88HMI4BIOW5LpOwHVnibSvqeczVtHjaSDv7/R7TND3q3nXj0jySLmXVex1muPpxMW8HaEKwZszqs2KuNGDroOw7Xsc50i7qRNYU5pEYlpRa24FyOWehjefSGQWhAKd1hcGAxkmJMdZKU5Hpj9X42/h1o4PgpTfmyWLA+6nZfqdmZgxxwG67w+gdXHCCjyh4zKWASwZAwm4F3rvXL9tpzxc4cJp9eOy950a0P6pR5dN7+dC5Hru+L/rbFx1PMh9EFCEG4b9i5v9OX35lYYH++1pf/wGAn+4+/lP62snBzP8ZM/8eZv49l5cXICcUzZQ+/FB6tx6A7q6xxvKbzahdl6WoqW8/51wrAXZOOiuZwTE6tC22foGYC98Wu6Hxli7M7w26tffqmXCllIrKAy2j0O/+9r19daeNRTduzSiipQABMVq1rFwP+46UkvTQMCzFPuOkxsPc+T4McxIYC5IvvrjwR0gVjJzIwRORSLWxeRiNCSlufKkaECLu2sabiJBTFgD4bKc8N4xgwMUIilFqUzqZdpADkRfw02v/DLQ5E4eI7XarZc/SXUvuR0BOBiOVhIJTNL9/Dmfz9705+p7rzi0rYs/JfqSb9Wmasj933WBqHkU+aL+Tc7Vqt2oqAPUHOi4txHvvaz54PCX7QAD+NIC/zcx/svvTXwTw+/X33w/gf+he/30kx+8F8K4LMz70LWAWso80FT0tHLKd0B6A8RSYSQEqyRisecW6zCBmeNcWc8kFjILNZkApWSXFpCjn/KH0Lm3PapPFFsEo2gyaEELEMIR6nbb4zZD0wFVP9z13+2q7dpwaw567YJ6L08lUmJGdxNqOIY1lGRXfMGZk74JWY+dav4acVzV02h/BO3hPslC46IIpet8MxwWeC1hFWxmAs+7fOavYLCASaYL1EEl8T4hwFEHaeLX3gkaVP7f7Nk+oFych8gjjFjlGJO8BZ2MZADeC/QZUlaMkW1RYCsAsfUpE8CigsgJ5AfIK4gwgoyBhxXpiED60OZ3/7VGcgKXPhsw2qr/bD5197tFzE0ma1Usth/Q2cVVRynmvSrRiGBlSsEZO+ltmS7eXp1uFp4QP/wiAfxnA3yCiv66v/VEA/yGAP09EfwDAr0EazQLAXwLwTwP4HoADgH/1KReS1hXrmjB08l+2yPpd1ya8941lWIVD1NVkZsyz1Bg455A5a01Fq/dnLSIx/QHDKOxY/r/2rifEuuSq/07Vve/119MfjDEShhh0ItlkpUMIWYRsBDXZjO6yMgvBTQRduBjJJlsFXQgiKAaiiNmomI3gHwRxYTTKZDIxTBI1oMOYUQQzfl+/d29VHRenTtWpevd19zfjzHsN9zRNv77v/jm3qs6p8/9ME5566hLBZDeOwwAaRzhja7CqjA2wsS3prJqghN0Tq0ooNpVZmZMSetG1XfXQeMNcyFExrCqTsmOUuGYT9nqxlGBvIzPt50a9MAZZa+Dsd1XLJGuocE3HVhyGYUAMXER3vbZ3rWlsRC03DyOj5+fHePBuILE/SOfniIAaV2Hx1XOXwBL/k0KjcqFjLguSgkIjeWA5KlK1C2t7sgbRN4PvXfo+/A2OazU/unA+A/j0kyISwowYApKrxG4zB63epcU91V0p1wdsL0U010Uq4n0NqAEn7HnC4KR1uG1Hr0SoFvMqdtVd23uP62nCQFSKigI149FKC8xcvB6Kk9ZlsAFKecwAoEgS4zgW8ZuZSziwvq/gLdGSjrmMhTNt223qteIoNnnk/1u1xwZHWSJW/HrRXqUYy3x6L0l/jTCItsKTfidqRSxSkxpdG4LKNMswOneeJyXwOYRynXyP0rovxghKEYnigfQJAAmCXw+3EdYSsS5BZ4eTMG1U9aI/t/fAWENlb/+xjEbXfX4QDOe8E5xFRKO+zNNPPw3iJDs5t8Efql+O45iLldZrVavWcy4vL3F9vcNms8X19bWI+eOIsLuGY0bYT5CCrZxFfI8YQyONABJgs59Sjr5Ti7sM/sXFBXa7XVMjwao8uxxNab0dGsyjkXrb7bZkVup11vqt+KRUG8GqbSLMAZEdLpxE4aXs5rvYXkjy2OCLhGH/hnkWu43oQAU3HT/rDqxEV/sPWI+PlegAFDVAz7XvpvYYzWL0fsA814jFlGpMgjJUmYOapThPuQO197i6eoiHD68wbDZAlB03sdT6nPa7A+/JPM8yXyki+pohq+9RmI8rpthFBtcziEZd6M6xRNofB5BzN9Iigfe/Ogd6fZVETT+OXn1RZiJc9M70eBZMQX3K+ZP85dpqvA7IoSGKmXMVoTl/R/kaCZ12EGPgHIL0hfQe42bE9eN9Lv8mNRqIPOZ5Aue6Dpl9wxEwDgNSqjt3MlKMMiJLRH4YSiVhb8RdFe2tNGHVDl2kem5KsVjlh2xAUyOm7KYeHgRyKIVSQYQ5BYQ54jL/rz5+62WJXGso6jsA1eCpzGnJF7+0q1mVo3ePKlhvidgrejGXG+KwTEkD28bNmF3WhDkmuJSQygYBwDjBUmJoLmgqQVQp25gM4bZLsXF12ne047BkfLwNDhnK4XF9Zi8h90xF54my6NSqgt38uBz2eUd48myJtwE0064sTnIApFyXGCHlV+olOqRc1ltjAuQewhBiZDx+NIGclxZxWY+ODDBJu/cpBlzP1yBiEIkRjRwAYiRIwQ6XC4hwiiDpJojNUN1h6tasz67ENceAmPXFkKqrUKMygTrp1sPQ+uhrEBCRaSenjCklDCwb/m7eI4Zcl5GAkKTyzj7MCOCSr4BkiV/ctrq4LA6WYSlYKcKKqjZysXfLAtX7obtaa3ew41bjEpZSw7338BsvDDAlMDvMOaQ8sZRzl4IlojLo89TlmmaJdWCHUruyzF2mGSICpeX4AD13SS1akh4OpIIlYs2uiJ7wj0kKdpxlc0oHEkCPHxHBDR40tv0iboKzkBR0x0gpIc4BGGQXsfq46ud24GzgTpEanKsBRpmwPDyIqt6pO3NeFg0uMUTMc8ilz3SBVDE7xiiMxrhF+3yBabeHci7VF222JmB8+sY42u/Sev+eYdRMUV+qLYdsI5imKYd+12xRbcSbWLIdVZ8PMSClGkilxHdMVz3mItVzFU9VlZZFYZffySEE26GLwPHQgFvckXnMpmmCIy+l8BbWEhE1rkgiyi3slxmdGPGoSA4pcYl7uIud4Cbo1YaemRy7xsISzuVc1LnpGYH9DEIJ+LoLnAVTSLnFGydGCtLizYq0ck5dLIC8bMkhcN5MQCbCJF2NwDcUJXG1BZclDDXmqe5mReN5njCQ5FHsdjtcXV0VteDBgwfF7y87swY5xZKBZ3VtoJ1kyxRUElIG1tcmIBLDWx/dqJ6BwjC9WvsZKGYtqUNgXX92IVlmrKCLz4Yt9+K13cns8dYupNGhCfv9ZN6/2mPsfFgJRdU2a3DrQY8UqQXiGmSIWy6ZFMuGQM2t7soMjhH2TdAzhSVVbEkS6RlLfX86MFT2EgfjboZQhbNgCgKVOMdscLMvZsVUKx0454r/WXRFKnUULrbb3M69zUJTQhtGSXLabDZN27dCwEY/DrkUmzIcv6DnFRE6183jxMVdVIw+2VPRi6d9SKp120nMxFh22ToWwWQsSlepqaRyK3ERxtGDnMYL5N8chCRZjUNDZHYcrC1C37OfGz1upQn710oNIPUOyZyHIOrVOGiPh5ap9cQvdR1cNQojM3hoKLfDMNeu0XqlIyqGTiwQSU+kS0S0pCbcRGz2u/5dHEl5dpMbViXdBWmiZwp17KlxLvRqS0pJysA/AQM7C5uCTJLD4HOdhEyUurNaI54N/tHFXLo3c2rOBdouQWWhUBu2axmMxgiooVF30pSkv6DuYEBtuKIEoKqAz+3b0xyFQbha5uzy8hJXV1fFHdoHMumvdmW2NRGUIWhTHGujUFxRiBWVuZBY/mMKIJh8CTMuVh3rP/f6bE8IlllbZiDXt5mPnCp+zG3uR2ktz4d6vTA4YY4giRnZ5LoMeo33DpvtFj7HrYCrm1fH6DZQxtXMh7KWPL69Hn9n4Ja4rYrU43DTOPTXLt1D0OXmPneF82AKDBBq9qHNGFTpQa3Vok/XqLN6fY4c8x4xiSrCDIQYsodhgMqIjgjEVafXpq9i8BTPhSPJCwghFuKzASIKip/iWnZFR2CSqD5iye0HSeu60aRHW9VE/6odQ4hNd0wlMildp5JLWThRArRUOpZ7SDy/NEphSKe4IjsXtUufrYxGjX0op9cQb1UBKmPggk8b+1CJn0hVlgBA1cJqZC2BTTK4Zfe3KiMRwQ+jKEEp5YjIbD+A8WRwDmDK4yYh2YMwZnCuHJUnAyJFFksjExxyYBRR+S2FW4hKAyDdNMi5EsVZIgLyd7qJcx7IBC7NeLmMywI53IHpFOaF4/dp3nQh/uIYnIf6QIB3JCXHaRBDD7U1BJyj3Em6LlRr7U4pYRwGDKNHTAk8SS8HzpKHJ4cQWZqaguGLs6pmTyoz0EDUeY5lJ9JajNbbYUV+IiotxB49epR7Q0QMG4L3jDnsEVLCPsyF6ZU+l6gqjRojQxgKl5dCJyr2aq0HL4uxKNEMSozRe6RxzIyBAfZIUSQfSnURqedG7R1lKswuZSUFYUbWLiNzoDacui65SCkSOq2G5ITEASB1z7bzp4yAc4Ngzp4Fmy06MWO3n0AbcRVzCmBSG05CTAFx9mKsZsaQpUTHyGNFSMSgpPUgMlNIUVoKJpGeuBsDNkkMyQwA+cwUkHflzC/UPqKxE0rAutZT3gBBKOHHVmU4MBSitR0VG0OnNvSbVZmoJOriXeEsmAIBxce+3WwQQq2fWAmjNcx5X0uCO5cLqADt7jP4rEumohY477M3oaoiNWCpDd7R+AJ7jMjlSkF1AqZpKpGI1X9MRaQnctm+QGUerYiu+Koas9/vm5JzQK3tYBeH5HDUZ7nMmBInaBSCjGFCSoDbjNL0JuTKVr6mkuu5tpSbBB4durJ6pqH6vV5nvQZFfM3NYK0qYb0sSjgp7/Q6hkUKA2Rhcyt6R+5xS2VnJO9zNGO4kSgKATmJauRU62Qu2TV0Pnpwja7fjtfi83DokehVSbtGLJTAqyILVJz6a465WI/BWagPKgbDLBbrvgNM2WwzSboI9/u9fJcXTejCV1V/3mw3tV6CrgMjxjtqIyel/mANMBImJENmjYW9u1AlASJqPAR2Z1S89LyaG1ClD7tLqsiu54ntpS3brp9tKrUyDH3XlKQegUb02XGy9hF7PwUbFq746zlLFYgaXZaP7Hb6DJKORin/gGovC2ZpDWgZqHWdWjyt5EZZvFfikjiGImouvqP2pOzf7xj0O3pveLX3scx06dnWs3aTwfMmXHrJwhpt7wpnISkAMtEPLi+xv5Yw1dbCehibL/prNXJtNhtM84wtEWIIINcWQFXmwSy9C9V3T0TFUBlirSYcY0TUSsKuShva7FYJQY1/mpWo+EpXKoeLC+lGPIeAxFRatyvz0YVgXXl299VwXxXTLRQCyBKG5mPEGAFvjE/GeAg1XoIxDocdqfpFZHef23ZPPd9KAxnTA7XEltgfvLiGZ1u5GfU8uQNEZKe2fsPSWi+EicrQJZDJAQ7F7nLAmPQYDpPBlhjQTZ974rfj1atm/dj15y3haXFVlfDYPD4JQwDOhCmo8Sqa5igqjgOtWGv9/TEe9lPYbreYQg0l1l1/2u/x+NFjcAqIITaWd+m8NDSLWYnRe1+9B7mUmN2xgKX6kK48V7IwJcRawYq/NqHIisxWNK8E1lZ/1p3QurFqnERVY2ImnuhraXdGDcayBlS7+Hox/zax2IJeY92CCipNFQmKWQqzcMqGO1eyB/X5TbEYoBiA4arHIoaI5GuBWWUeznlpAbCw7o4xtzqWx0V8e5+bGIJe36sclnFaoraSWM+krQQhthgUyaeXpvv/7wpnwRQEZKHu97siomtUY0qhLCRlCK3OZfszeIBJjGpedC4tAOIHQpx0YtuU3xhryKhOnjCgGrG4GTeYrndikBoqIWlAj+5qw+ARo3guvNtIX0UbT59z4dX4FTJTKROt6oyrJd/Fx24akziJQZA6B9l+Ac75HIQQxdINp8TNgIZc02HAy7HFZzMJVeKyY6TXqp09pgCX2t1Td2y9pw0IK/puEi+Ry25pTlys9iUWhaXy8xRnzGHCPO3hNxfZ01QJoahzGc9p2uN/33gDHHOEKdpdtd+x7fFeYl1cuXdglnqPXnro/1/6e5uEoePY2xGeVEJQOAubApATVkKAI4eNiSYUW63E/yuRCDOolnqfKxM7T2VnTjFJGfAsVidOuLjYYtzktGsaOmYiFulerAeq0UmTsja5zZjaBOxE2UXk3QjvN/B+C0IVR0OMmFMEeS+l4/iwvdeSfmjf35GTRE+SztkglysH5QxIIkjBjQRQzi1IqdhviKgYUnsxtTVYHS5OK7mlJA16E0schBZlOZgvrqLsQYQpRNQnT2LHYWA/z5jDLIVExgGcJZzECbtph8fXj7Hf7YqNpNmtmaE/w+ARwoz9bgdElozKjhh7demYStAapXFwjpWs+gCvY6qBQi/621ySA1ox0kUv0dl72GcvGUaPwdlICswSYUh4AOQYgnGU+ASwVEny3ibYWFdiHgRX6wqo2KqL0QY9yQRJTQH1zV9dXTXGLWUQ2sCkljljzKFWd97v9yXQygb26CTLcwkeDsEQP7ga7jTEG67tIq32Dk3BVsmoBGClWnqd5aEyFpr4RMKAUmL4/E4i0mfm51trdb8jymJraw/0O6c8LxbDZYwJ44isQh16G+ziLQZDJ56CjScgu4KZE8hvcrv5bJTN6iUATPu9FOVJCTftbeQcYgxZhUpAZDi0thQLsp7apLV+nR4Tx4/p//2YWftKwbNTH9pEv3p/+xxyJOVCO6ZoGYn1YN0VzoMp5BfabLaY4k5a0CfxzccYRXQ07kJA9S4qIq0zuuWQw2ULpyTCxcWFEPk0g1MNirG6rzUwEVXjnt05iZxUvqJaEMVGVi4tDOckc1JFdmtU3Gw22XgpYn9J1up2C2WGVvSOMQpTyLUh1NIeU4QbPMDITXEVpxxy7BwianzHTbq1jEG7sPvFLbypNcrZcbA7sWUITUSel5Ji2nxmHEdsLy7EvZq7Wel99HzbP6KC8ffnF9AKVQF1N18iFJ0rDzoYD50P/dxfp8ePMYyekfbXLkkb9jrLNMqalJ1g8f5WOrm3kgKpgQziz5fing/q90TNAk6JCyMQYpUwWZtARFRtD7rjhhCAmJp2aGokDLnqEqC7p/SoVMYhzwFAtcirPs/2pVDDo3Dyw54N+plZshovthcSNGN0Yo221IUgqktVV/R39GPxVEhsRu2fwCwlzkQFSIjKRJPUXnDusBycXdTV9edKLQcpcNOGQoOoaZSjjLUnvl6E1mcr44aTzEkdR/IEDsiRgOKmVHtAUzVJVRxasmH0qkBrMGy/y/h0a1PfRT9bhtETYxOncgMcl8yqO17VL/2uxzX/V5i2ZRqWGdiN7S5Ab9YY8f8JRPSfAB4B+K9T4/IW4N243/gD9/8d7jv+wNv7Dj/AzN9320lnwRQAgIi+zMwfOjUebxbuO/7A/X+H+44/cB7vcDbehxVWWOE8YGUKK6ywQgPnxBR+69QIvEW47/gD9/8d7jv+wBm8w9nYFFZYYYXzgHOSFFZYYYUzgJMzBSL6CSJ6hYi+RUQvnBqfuwIRfZuIvkpELxLRl/OxdxHRnxPRN/Pf7zk1nhaI6HNE9DoRvWyOLeJMAr+e5+UlInrudJgXXJfw/ywRvZrn4UUi+oT57pcy/q8Q0Y+fBusKRPQ+IvorIvonIvoaEf18Pn5ec2ADYd7pXwAewD8DeD+ADYCvAPjgKXF6Aty/DeDd3bFfAfBC/vwCgF8+NZ4dfh8D8ByAl2/DGdIP9E8h0T4fAfClM8X/swB+ceHcD+b1tAXwbF5n/sT4PwPgufz5IYBvZDzPag5OLSl8GMC3mPlfmHkC8AUAz58Yp7cCzwP4fP78eQA/eUJcDoCZ/xrAf3eHj+H8PIDfZYG/BfA0ET3zzmC6DEfwPwbPA/gCM++Z+V8hDY8//LYhdwdg5teY+R/z5zcAfB3Ae3Fmc3BqpvBeAP9m/v/3fOw+AAP4MyL6ByL62XzsPcz8Wv78HwDecxrUngiO4Xyf5ubnsnj9OaOynTX+RPSDAH4EwJdwZnNwaqZwn+GjzPwcgI8D+DQRfcx+ySL/3SvXzn3EGcBvAvghAD8M4DUAv3padG4HIroC8IcAfoGZv2u/O4c5ODVTeBXA+8z/35+PnT0w86v57+sA/hgimn5Hxbv89/XTYXhnOIbzvZgbZv4OM0eWfOffRlURzhJ/IhohDOH3mfmP8uGzmoNTM4W/B/ABInqWiDYAPgngiyfG6VYgoqeI6KF+BvBjAF6G4P6pfNqnAPzJaTB8IjiG8xcB/HS2gH8EwP8YEfdsoNOxfwoyD4Dg/0ki2hLRswA+AODv3mn8LJCkN/4OgK8z86+Zr85rDk5pjTUW1m9ArMOfOTU+d8T5/RDL9lcAfE3xBvC9AP4SwDcB/AWAd50a1w7vP4CI2DNEP/2ZYzhDLN6/keflqwA+dKb4/17G7yUIET1jzv9Mxv8VAB8/A/w/ClENXgLwYv79xLnNwRrRuMIKKzRwavVhhRVWODNYmcIKK6zQwMoUVlhhhQZWprDCCis0sDKFFVZYoYGVKaywwgoNrExhhRVWaGBlCiussEID/wdv4TEYs1sONAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#载入图像方法\n",
    "def image_load(img_path):\n",
    "    photo=Image.open(img_path)\n",
    "    photo=photo.resize([224,224])  #图像统一大小\n",
    "    im=np.array(photo).astype(\"float32\")    #图像数字化\n",
    "    # im=np.where(im > 150, 0, im)        #把像素点大于150的置为0\n",
    "    im=im/255                          #归一化\n",
    "    im=paddle.to_tensor(im)            #张量\n",
    "    return im\n",
    "im=image_load(\"data/garbage/valid/29/img_14911.jpg\")     #调用载入图像方法\n",
    "plt.imshow(im)      #显示图像\n",
    "im=im.reshape([1,3,224,224])   #图像变形为1张1通道28*28大小\n",
    "model.load(\"mymodel2/resnet\")   #加载模型参数\n",
    "result=model.predict_batch(im)  #使用model.predict_batch对图像进行预测\n",
    "print(file_dict[f\"{np.argmax(result)}\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc-showmarkdowntxt": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
